code: def fetch_imagenet_class(
      path, class_name, number_of_images, imagenet_dataframe
  ):
      if imagenet_dataframe is None:
          raise tf.errors.NotFoundError(
              None,
              None,
              "Please provide a dataframe containing the imagenet classes. Easiest way to do this is by calling make_imagenet_dataframe()",
          )
      # To speed up imagenet download, we timeout image downloads at 5 seconds.
      socket.setdefaulttimeout(5)
  
      tf.compat.v1.logging.info(
          "Fetching imagenet data for " + class_name
      )
      concept_path = os.path.join(path, class_name)
      tf.io.gfile.makedirs(concept_path)
      tf.compat.v1.logging.info("Saving images at " + concept_path)
  
      # Check to see if this class name exists. Fetch all urls if so.
      all_images = fetch_all_urls_for_concept(
          imagenet_dataframe, class_name
      )
  
      # Fetch number_of_images images or as many as you can.
      num_downloaded = 0
      for image_url in all_images:
          if "flickr" not in image_url:
              try:
                  download_image(concept_path, image_url)
                  num_downloaded += 1
  
              except Exception as e:
                  tf.compat.v1.logging.info(
                      "Problem downloading imagenet image. Exception was "
                      + str(e)
                      + " for URL "
                      + image_url
                  )
          if num_downloaded >= number_of_images:
              break
  
      # If we reached the end, notify the user through the console.
      if num_downloaded < number_of_images:
          print(
              "You requested "
              + str(number_of_images)
              + " but we were only able to find "
              + str(num_downloaded)
              + " good images from imageNet for concept "
              + class_name
          )
      else:
          print(
              "Downloaded "
              + str(number_of_images)
              + " for "
              + class_name
          )
  
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 4 for calling a global function with example usage: fetch_imagenet_class(path, class_name, number_of_images, imagenet_dataframe) without return types