code: def get_bottleneck_path(image_lists, label_name, index, bottleneck_dir,
                          category, module_name):
    
    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'
docs: 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.