model_card_toolkit/utils/tfx_util.py [206:230]:
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    store: mlmd.MetadataStore,
    model_id: int,
    pipeline_types: Optional[PipelineTypes] = None
) -> List[metadata_store_pb2.Artifact]:
  """Gets a list of evaluation artifacts from a model artifact.

  It looks for the evaluator component runs that take the given model as input.
  Then it returns the metrics artifact of that component run.

  Args:
    store: A ml-metadata MetadataStore to look for evaluation metrics.
    model_id: The id for the model artifact in the `store`.
    pipeline_types: An optional set of types if the `store` uses custom types.

  Returns:
    A list of metrics artifacts produced by the Evaluator component runs
    which take the given model artifact as the input.

  Raises:
    ValueError: If the `model_id` cannot be resolved as a model artifact in the
      given `store`.
  """
  if not pipeline_types:
    pipeline_types = _get_tfx_pipeline_types(store)
  _validate_model_id(store, pipeline_types.model_type, model_id)
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model_card_toolkit/utils/tfx_util.py [236:261]:
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    store: mlmd.MetadataStore,
    model_id: int,
    pipeline_types: Optional[PipelineTypes] = None
) -> List[metadata_store_pb2.Artifact]:
  """Gets a list of statistics artifacts from a model artifact.

  It first looks for the input datasets of the trainer that produces the model.
  If the model uses a transformed examples, it further looks for the original
  dataset. Then it returns the statistics artifact of the found dataset(s).

  Args:
    store: A ml-metadata MetadataStore instance.
    model_id: The id for the model artifact in the `store`.
    pipeline_types: An optional set of types if the `store` uses custom types.

  Returns:
    A list of statistics artifacts produced by the StatsGen component runs
    for the datasets which are used to train the model.

  Raises:
    ValueError: If the `model_id` cannot be resolved as a model artifact in the
      given `store`.
  """
  if not pipeline_types:
    pipeline_types = _get_tfx_pipeline_types(store)
  _validate_model_id(store, pipeline_types.model_type, model_id)
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