code: def get_image_path(
      image_lists, label_name, index, image_dir, category
  ):
      """Returns a path to an image for a label at the given index.
  
      Args:
        image_lists: OrderedDict of training images for each label.
        label_name: Label string we want to get an image for.
        index: Int offset of the image we want. This will be moduloed by the
        available number of images for the label, so it can be arbitrarily large.
        image_dir: Root folder string of the subfolders containing the training
        images.
        category: Name string of set to pull images from - training, testing, or
        validation.
  
      Returns:
        File system path string to an image that meets the requested parameters.
  
      """
      if label_name not in image_lists:
          logging.fatal("Label does not exist %s.", label_name)
      label_lists = image_lists[label_name]
      if category not in label_lists:
          logging.fatal("Category does not exist %s.", category)
      category_list = label_lists[category]
      if not category_list:
          logging.fatal(
              "Label %s has no images in the category %s.",
              label_name,
              category,
          )
      mod_index = index % len(category_list)
      base_name = category_list[mod_index]
      sub_dir = label_lists["dir"]
      full_path = os.path.join(image_dir, sub_dir, base_name)
      return full_path
  
imports: from absl import logging
  import argparse
  import collections
  from datetime import datetime
  import hashlib
  import os.path
  import random
  import re
  import sys
  import numpy
  import tensorflow
  import tensorflow_hub
  from tensorflow.contrib import quantize
label_desc: Use image node_id 2 for calling a global function with example usage: get_image_path(image_lists, label_name, index, image_dir, category) and returns: full_path