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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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:
    tf.logging.fatal('Label does not exist %s.', label_name)
  label_lists = image_lists[label_name]
  if category not in label_lists:
    tf.logging.fatal('Category does not exist %s.', category)
  category_list = label_lists[category]
  if not category_list:
    tf.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