def METHOD_NAME(
    sampled_key: Text,
    size: int,
    name: Text,
    random_seed: Optional[int],
    model_names: Optional[List[Text]] = None,
    output_names: Optional[List[Text]] = None,
    sub_keys: Optional[List[metric_types.SubKey]] = None,
    example_weighted: bool = False) -> metric_types.MetricComputations:
  """Returns metrics computations for FixedSizeSample metrcs."""
  keys = []
  for model_name in model_names or ['']:
    for output_name in output_names or ['']:
      for sub_key in sub_keys or [None]:
        keys.append(
            metric_types.MetricKey(
                name,
                model_name=model_name,
                output_name=output_name,
                sub_key=sub_key,
                example_weighted=example_weighted))
  return [
      metric_types.MetricComputation(
          keys=keys,
          preprocessors=[
              metric_types.FeaturePreprocessor(feature_keys=[sampled_key])
          ],
          combiner=_FixedSizeSampleCombineFn(
              metric_keys=keys,
              sampled_key=sampled_key,
              size=size,
              example_weighted=example_weighted,
              random_seed=random_seed))
  ]