code: def get_bottleneck_path(
      image_lists,
      label_name,
      index,
      bottleneck_dir,
      category,
      module_name,
  ):
      """Returns a path to a bottleneck file 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: Integer 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.
        bottleneck_dir: Folder string holding cached files of bottleneck values.
        category: Name string of set to pull images from - training, testing, or
        validation.
        module_name: The name of the image module being used.
  
      Returns:
        File system path string to an image that meets the requested parameters.
      """
      module_name = (
          module_name.replace("://", "~")  # URL scheme.
          .replace("/", "~")  # URL and Unix paths.
          .replace(":", "~")
          .replace("\\", "~")
      )  # Windows paths.
      return (
          get_image_path(
              image_lists, label_name, index, bottleneck_dir, category
          )
          + "_"
          + module_name
          + ".txt"
      )
  
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 3 for calling a global function with example usage: get_bottleneck_path(image_lists, label_name, index, bottleneck_dir, category, module_name) and returns: unknown