def METHOD_NAME(
    image_id_feature) -> label_lib.LabelFnType:
  """Creates a simple labeler that works with (many) image-caption datasets.
  This labeler assumes each example in the dataset contains one image. If it
  sees two distinct in-batch examples with same image (as identified by the
  `image_id_feature`), the labeler marks the pair to be dropped from the loss
  calculation.
  This has two high-level effects:
    - Duplicate-example pairs are dropped. This is good; it means duplicates
      aren't incorrectly assigned negative labels.
    - Any co-caption pairs (examples with the same image but different captions)
      are also dropped. This is non-optimal but fine in most cases. (Ideally
      image -> caption pairs from these examples should get positive labels,
      but doing so requires a more specialized labeling function. Dropping them
      is easier and not wrong.)
  Args:
    image_id_feature: Name of a scalar feature that uniquely identifies the
      image in each example.
  Returns:
    The instantiated label function (callable).
  """
  return label_lib.ExamplePairLabeler(drop_pairs_that_match=image_id_feature)