code: def extract_mind(
      train_zip,
      valid_zip,
      train_folder="train",
      valid_folder="valid",
      clean_zip_file=True,
  ):
      """Extract MIND dataset
  
      Args:
          train_zip (str): Path to train zip file
          valid_zip (str): Path to valid zip file
          train_folder (str): Destination forder for train set
          valid_folder (str): Destination forder for validation set
  
      Returns:
          str, str: Train and validation folders
      """
      root_folder = os.path.basename(train_zip)
      train_path = os.path.join(root_folder, train_folder)
      valid_path = os.path.join(root_folder, valid_folder)
      unzip_file(train_zip, train_path, clean_zip_file=clean_zip_file)
      unzip_file(valid_zip, valid_path, clean_zip_file=clean_zip_file)
      return train_path, valid_path
  
imports: import os
  import random
  import logging
  import json
  import numpy
  import re
  from tqdm import tqdm
  from nltk.tokenize import RegexpTokenizer
  from recommenders.datasets.download_utils import maybe_download, download_path, unzip_file
label_desc: Use image node_id 2 for calling a global function with example usage: extract_mind(train_zip, valid_zip, train_folder, valid_folder, clean_zip_file) and returns: train_path,  valid_path