code: def generate_random_folders(
      working_directory,
      random_folder_prefix,
      number_of_random_folders,
      number_of_examples_per_folder,
      imagenet_dataframe,
  ):
      imagenet_concepts = imagenet_dataframe[
          "class_name"
      ].values.tolist()
      for partition_number in range(number_of_random_folders):
          partition_name = (
              random_folder_prefix + "_" + str(partition_number)
          )
          partition_folder_path = os.path.join(
              working_directory, partition_name
          )
          tf.io.gfile.makedirs(partition_folder_path)
  
          # Select a random concept
          examples_selected = 0
          while examples_selected < number_of_examples_per_folder:
              random_concept = random.choice(imagenet_concepts)
              urls = fetch_all_urls_for_concept(
                  imagenet_dataframe, random_concept
              )
              for url in urls:
                  # We are filtering out images from Flickr urls, since several of those were removed
                  if "flickr" not in url:
                      try:
                          download_image(partition_folder_path, url)
                          examples_selected += 1
                          if (examples_selected) % 10 == 0:
                              tf.compat.v1.logging.info(
                                  "Downloaded "
                                  + str(examples_selected)
                                  + "/"
                                  + str(number_of_examples_per_folder)
                                  + " for "
                                  + partition_name
                              )
                          break  # Break if we successfully downloaded an image
                      except:
                          pass
  
file_path: tcav/tcav/tcav_examples/image_models/imagenet/imagenet_and_broden_fetcher.py
imports: import pandas
  import urllib.request
  import os
  import shutil
  import PIL
  from PIL import Image
  import tensorflow
  import socket
  import random
label_desc: Use image node_id 6 for calling a global function with example usage: generate_random_folders(working_directory, random_folder_prefix, number_of_random_folders, number_of_examples_per_folder, imagenet_dataframe) without return types