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.

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'