Step_4_Train_local_model_(corrected).ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNpiS6M9Q/N3L9KEZneJO6G",
      "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/Geek-a-Byte/thesis/blob/main/Step_4_Train_local_model_(corrected).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "_32lbSvKLqgN"
      },
      "outputs": [],
      "source": [
        "!pip install -q streamlit"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!npm install localtunnel"
      ],
      "metadata": {
        "id": "TaMO67r5LuPR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile app.py\n",
        "# Import required modules\n",
        "import gspread\n",
        "from oauth2client.service_account import ServiceAccountCredentials\n",
        "from pprint import pprint\n",
        "\n",
        "import streamlit as st\n",
        "from io import StringIO\n",
        "import shutil\n",
        "import os\n",
        "\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D\n",
        "from tensorflow.keras.layers import MaxPooling2D\n",
        "from tensorflow.keras.layers import Activation\n",
        "from tensorflow.keras.layers import Flatten\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "from tensorflow.keras import backend as K\n",
        "import collections\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import random\n",
        "import cv2\n",
        "import os\n",
        "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' \n",
        "from imutils import paths\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "scope = [\"https://spreadsheets.google.com/feeds\", 'https://www.googleapis.com/auth/spreadsheets',\n",
        "\t\t\"https://www.googleapis.com/auth/drive.file\", \"https://www.googleapis.com/auth/drive\"]\n",
        "\n",
        "\n",
        "\n",
        "# Assign credentials ann path of style sheet\n",
        "creds = ServiceAccountCredentials.from_json_keyfile_name(\"/content/thesis-367714-64137e9747ad.json\", scope)\n",
        "client = gspread.authorize(creds)\n",
        "spreadsheet = client.open(\"thesis_aliya\")\n",
        "sheet1 = spreadsheet.sheet1\n",
        "sheet2 = spreadsheet.worksheet('Sheet2')\n",
        "sheet3 = spreadsheet.worksheet('Sheet3')\n",
        "sheet4 = spreadsheet.worksheet('Sheet4')\n",
        "sheet5 = spreadsheet.worksheet('Sheet5')\n",
        "sheet6 = spreadsheet.worksheet('Sheet6')\n",
        "\n",
        "def load(paths, verbose=-1):\n",
        "    '''expects images for each class in seperate dir'''\n",
        "    data = list()\n",
        "    labels = list()\n",
        "    # loop over the input images\n",
        "    for (i, imgpath) in enumerate(paths):\n",
        "        # load the image and extract the class labels\n",
        "        im_gray = cv2.imread(imgpath, cv2.IMREAD_GRAYSCALE)\n",
        "        image = np.array(im_gray).flatten()\n",
        "        label = imgpath.split(os.path.sep)[-2] # Metaplastic, Parabasal, Koilocytotic, Superficial-Intermediate, Dyskeratotic\n",
        "        # scale the image to [0, 1] and add to list\n",
        "        data.append(image/255)\n",
        "        labels.append(label)\n",
        "        # show an update every `verbose` images\n",
        "        if verbose > 0 and i > 0 and (i + 1) % verbose == 0:\n",
        "            print(label);\n",
        "            print(\"[INFO] processed {}/{}\".format(i + 1, len(paths)))\n",
        "    # return a tuple of the data and labels\n",
        "    return data, labels\n",
        "\n",
        "class SimpleMLP:\n",
        "    @staticmethod\n",
        "    def build(shape, classes):\n",
        "        model = Sequential()\n",
        "        model.add(Dense(200, input_shape=(shape,)))\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(Dense(200))\n",
        "        model.add(Activation(\"relu\"))\n",
        "        model.add(Dense(classes))\n",
        "        model.add(Activation(\"softmax\"))\n",
        "        return model\n",
        "\n",
        "#process and batch the training data for each client\n",
        "clients_batched = dict()\n",
        "\n",
        "comms_round = 100\n",
        "\n",
        "#create optimizer\n",
        "lr = 0.01 \n",
        "loss='categorical_crossentropy'\n",
        "metrics = ['accuracy'] # f1_score, precision_score\n",
        "optimizer = SGD(lr=lr, \n",
        "                decay=lr / comms_round, \n",
        "                momentum=0.9\n",
        "               ) \n",
        "\n",
        "\n",
        "# Function for preparing client data for training\n",
        "def batch_data(data_shard, bs=32):\n",
        "    '''Takes in a clients data shard and create a tfds object off it\n",
        "    args:\n",
        "        shard: a data, label constituting a client's data shard\n",
        "        bs:batch size\n",
        "    return:\n",
        "        tfds object'''\n",
        "    #seperate shard into data and labels lists\n",
        "    data, label = zip(*data_shard)\n",
        "    dataset = tf.data.Dataset.from_tensor_slices((list(data), list(label)))\n",
        "    return dataset.shuffle(len(label)).batch(bs)\n",
        "\n",
        "def weight_scalling_factor(clients_trn_data, client_name):\n",
        "    client_names = list(clients_trn_data.keys())\n",
        "    #get the bs\n",
        "    bs = list(clients_trn_data[client_name])[0][0].shape[0]\n",
        "    #first calculate the total training data points across clinets\n",
        "    global_count = sum([tf.data.experimental.cardinality(clients_trn_data[client_name]).numpy() for client_name in client_names])*bs\n",
        "    # get the total number of data points held by a client\n",
        "    local_count = tf.data.experimental.cardinality(clients_trn_data[client_name]).numpy()*bs\n",
        "    return local_count/global_count\n",
        "\n",
        "def scale_model_weights(weight, scalar):\n",
        "    '''function for scaling a models weights'''\n",
        "    weight_final = []\n",
        "    steps = len(weight)\n",
        "    for i in range(steps):\n",
        "        weight_final.append(scalar * weight[i])\n",
        "    return weight_final\n",
        "\n",
        "\n",
        "def train_single_model(global_model,client,image_list,label_list):\n",
        "    data = list(zip(image_list, label_list))\n",
        "    random.shuffle(data)\n",
        "    \n",
        "    smlp_local = SimpleMLP()\n",
        "    local_model = smlp_local.build(4356, 5)\n",
        "    local_model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n",
        "    \n",
        "    # get the global model's weights - will serve as the initial weights for all local models\n",
        "    global_weights = global_model.get_weights()\n",
        "    \n",
        "    # set local model weight to the weight of the global model\n",
        "    local_model.set_weights(global_weights)\n",
        "    \n",
        "    # batch the data\n",
        "    clients_batched[client] = batch_data(data)\n",
        "    \n",
        "    #fit local model with client's data\n",
        "    tf.autograph.experimental.do_not_convert(local_model.fit(clients_batched[client], epochs=1, verbose=0))\n",
        "    \n",
        "    steps = len(local_model.get_weights())\n",
        "    st.write(steps)\n",
        "\n",
        "    #scale the model weights and add to list\n",
        "    scaling_factor = weight_scalling_factor(clients_batched, client)\n",
        "    scaled_weights = scale_model_weights(local_model.get_weights(), scaling_factor)\n",
        "    \n",
        "    sheets=[sheet1,sheet2,sheet3,sheet4,sheet5,sheet6]\n",
        "\n",
        "    for i in range(0,len(scaled_weights)):\n",
        "      if(i%2==0):\n",
        "        arr = scaled_weights[i]\n",
        "        ar = np.around(arr.tolist(), decimals=3)\n",
        "        sheets[i].update('A1', ar.tolist())\n",
        "        st.write(ar) \n",
        "      else:\n",
        "        arr = scaled_weights[1]\n",
        "        ar = np.around(arr, decimals=3)\n",
        "        sheets[i].insert_row(ar.tolist(),1)\n",
        "        st.write(ar) \n",
        "\n",
        "    #clear session to free memory after each communication round\n",
        "    K.clear_session()\n",
        "\n",
        "    return scaled_weights\n",
        "    \n",
        "import zipfile\n",
        "\n",
        "app_mode = st.sidebar.selectbox('Select Page',['Home','About'])\n",
        "if app_mode=='Home': \n",
        "    st.title('Classify Cervical Cancer') \n",
        "    st.markdown('upload photographs in bulk :')\n",
        "    #adding a single-line text input widget\n",
        "    name = st.text_input('Enter the id of your institution: ', '0')\n",
        "    #displaying the entered text\n",
        "    st.write('the name is ', name) \n",
        "    if(os.path.isdir('/content/content/Client_'+name)==True):\n",
        "          shutil.rmtree('/content/content/Client_'+name)\n",
        "    uploaded_file = st.file_uploader(\"Choose a file\",type=\"zip\")\n",
        "    if uploaded_file is not None:\n",
        "      with zipfile.ZipFile(uploaded_file, \"r\") as z:\n",
        "        z.extractall(\".\")\n",
        "   \n",
        "      # To convert to a string based IO:\n",
        "      # stringio = StringIO(uploaded_file.getvalue().decode(\"utf-8\"))\n",
        "\n",
        "      # To read file as string:\n",
        "      # string_data = stringio.read()\n",
        "\n",
        "    \n",
        "    #adding a button\n",
        "    col1, col2, col3 = st.columns([1,1,1])\n",
        "    with col1:\n",
        "        train = st.button('Train')\n",
        "    with col2:\n",
        "        test = st.button('Test')\n",
        "    with col3:\n",
        "        aggregate = st.button('Aggregate')\n",
        "    if test:\n",
        "       st.write('testing') #displayed when the button is clicked\n",
        "    if train:\n",
        "       #declear path to your data folder\n",
        "       img_path = '/content/content/Client_'+name\n",
        "       \n",
        "       #get the path list using the path object\n",
        "       image_paths = list(paths.list_images(img_path))\n",
        "\n",
        "       #apply our function\n",
        "       image_list, label_list = load(image_paths, verbose=10000)\n",
        "           \n",
        "       #binarize the labels\n",
        "       lb = LabelBinarizer()\n",
        "       label_list = lb.fit_transform(label_list)\n",
        "\n",
        "       #split data into training and test set\n",
        "       X_train, X_test, y_train, y_test = train_test_split(image_list, \n",
        "                                                    label_list, \n",
        "                                                    test_size=0.1, \n",
        "                                                    random_state=42)\n",
        "       smlp_global = SimpleMLP()\n",
        "       global_model = smlp_global.build(4356, 5)\n",
        "       train_single_model(global_model,'aliya',image_list,label_list)\n",
        "       st.write('training') #displayed when the button is clicked\n",
        "    \n",
        "    if aggregate:\n",
        "       st.write('aggregating') #displayed when the button is clicked\n",
        "\n",
        "if app_mode=='About': \n",
        "      st.title('Classify Cervical Cancer') \n",
        " \n",
        "\n",
        " "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PcTd3FViLvi9",
        "outputId": "6f857513-d69a-4873-e5ef-42f1e882be84"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting app.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!streamlit run /content/app.py &>/content/logs.txt &"
      ],
      "metadata": {
        "id": "P3NZWg18LxUI"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!npx localtunnel --port 8501"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b6nt_FbNLyh5",
        "outputId": "0dcca1c1-82bf-4acb-8954-feabbb6e41ed"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K\u001b[?25hnpx: installed 22 in 1.528s\n",
            "your url is: https://pretty-stars-exist-35-245-13-165.loca.lt\n",
            "^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import required modules\n",
        "import gspread\n",
        "from oauth2client.service_account import ServiceAccountCredentials\n",
        "from pprint import pprint\n",
        "\n",
        "scope = [\"https://spreadsheets.google.com/feeds\", 'https://www.googleapis.com/auth/spreadsheets',\n",
        "\t\t\"https://www.googleapis.com/auth/drive.file\", \"https://www.googleapis.com/auth/drive\"]\n",
        "\n",
        "\n",
        "\n",
        "# Assign credentials ann path of style sheet\n",
        "creds = ServiceAccountCredentials.from_json_keyfile_name(\"/content/thesis-367714-64137e9747ad.json\", scope)\n",
        "client = gspread.authorize(creds)\n",
        "sheet = client.open(\"thesis_aliya\").worksheet('second_weight')\n",
        "\n",
        "\n",
        "\n",
        "# display data\n",
        "data = sheet.get_all_records()\n",
        "row4 = sheet.row_values(4)\n",
        "col2 = sheet.col_values(2)\n",
        "cell = sheet.cell(5, 2).value\n",
        "\n",
        "print(\"Column 2 Data : \")\n",
        "pprint(col2)\n",
        "print(\"\\nRow 4 Data : \")\n",
        "pprint(row4)\n",
        "print(\"\\nCell (5,2) Data : \")\n",
        "pprint(cell)\n",
        "print(\"\\nAll Records : \")\n",
        "pprint(data)\n",
        "\n",
        "\n",
        "\n",
        "# Inserting data\n",
        "insertRow = [6, \"Soumodeep Naskar\", \"Purple\"]\n",
        "sheet.insert_row(insertRow, 4)\n",
        "print(\"\\nAll Records after inserting new row : \")\n",
        "pprint(data)\n",
        "\n",
        "\n",
        "\n",
        "# Deleting data\n",
        "sheet.delete_row(7)\n",
        "print(\"\\nAll Records after deleting row 7 : \")\n",
        "pprint(data)\n",
        "\n",
        "\n",
        "\n",
        "# Update a cell\n",
        "sheet.update_cell(5, 2, \"Nitin Das\")\n",
        "print(\"\\nAll Records after updating cell (5,2) : \")\n",
        "pprint(data)\n",
        "\n",
        "\n",
        "\n",
        "# Display no. of rows, columns\n",
        "# and no. of rows having content\n",
        "numRows = sheet.row_count\n",
        "numCol = sheet.col_count\n",
        "print(\"Number of Rows : \", numRows)\n",
        "print(\"Number of Columns : \", numCol)\n",
        "print(\"Number of Rows having content : \", len(data))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "id": "MQxMmSYbPRJz",
        "outputId": "5577b3da-0e89-4edd-b478-2c84dcc4c76a"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column 2 Data : \n",
            "[]\n",
            "\n",
            "Row 4 Data : \n",
            "[]\n",
            "\n",
            "Cell (5,2) Data : \n",
            "''\n",
            "\n",
            "All Records : \n",
            "[]\n",
            "\n",
            "All Records after inserting new row : \n",
            "[]\n",
            "\n",
            "All Records after deleting row 7 : \n",
            "[]\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    376\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Python 2.7, use buffering of HTTP responses\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 377\u001b[0;31m                 \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffering\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    378\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Python 3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: getresponse() got an unexpected keyword argument 'buffering'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-34-7bbcdeedebaa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;31m# Update a cell\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0msheet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_cell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Nitin Das\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\nAll Records after updating cell (5,2) : \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0mpprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/gspread/models.py\u001b[0m in \u001b[0;36mupdate_cell\u001b[0;34m(self, row, col, value)\u001b[0m\n\u001b[1;32m    890\u001b[0m             },\n\u001b[1;32m    891\u001b[0m             body={\n\u001b[0;32m--> 892\u001b[0;31m                 \u001b[0;34m'values'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    893\u001b[0m             }\n\u001b[1;32m    894\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/gspread/models.py\u001b[0m in \u001b[0;36mvalues_update\u001b[0;34m(self, range, params, body)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \"\"\"\n\u001b[1;32m    240\u001b[0m         \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSPREADSHEET_VALUES_URL\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquote\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'put'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    242\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/gspread/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, endpoint, params, data, json, files, headers)\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m             \u001b[0mfiles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfiles\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m             \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m         )\n\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36mput\u001b[0;34m(self, url, data, **kwargs)\u001b[0m\n\u001b[1;32m    588\u001b[0m         \"\"\"\n\u001b[1;32m    589\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 590\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'PUT'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    591\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/auth/transport/requests.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, data, headers, max_allowed_time, timeout, **kwargs)\u001b[0m\n\u001b[1;32m    484\u001b[0m                 \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest_headers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    485\u001b[0m                 \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 486\u001b[0;31m                 \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    487\u001b[0m             )\n\u001b[1;32m    488\u001b[0m         \u001b[0mremaining_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mguard\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremaining_timeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    528\u001b[0m         }\n\u001b[1;32m    529\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 530\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    532\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    641\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    642\u001b[0m         \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 643\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    645\u001b[0m         \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    447\u001b[0m                     \u001b[0mdecode_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    448\u001b[0m                     \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m                     \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    450\u001b[0m                 )\n\u001b[1;32m    451\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    598\u001b[0m                                                   \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout_obj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    599\u001b[0m                                                   \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 600\u001b[0;31m                                                   chunked=chunked)\n\u001b[0m\u001b[1;32m    601\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    602\u001b[0m             \u001b[0;31m# If we're going to release the connection in ``finally:``, then\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    378\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Python 3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    379\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 380\u001b[0;31m                     \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    381\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    382\u001b[0m                     \u001b[0;31m# Remove the TypeError from the exception chain in Python 3;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1371\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1372\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1373\u001b[0;31m                 \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1374\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1375\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mbegin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    317\u001b[0m         \u001b[0;31m# read until we get a non-100 response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 319\u001b[0;31m             \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    320\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36m_read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    278\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m         \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"status line\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    587\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 589\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    590\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    591\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1069\u001b[0m                   \u001b[0;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1070\u001b[0m                   self.__class__)\n\u001b[0;32m-> 1071\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1072\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1073\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m    927\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    928\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 929\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    930\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    931\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    }
  ]
}