{
            'input_tensor': ops.convert_to_tensor(sample),
        }
        for sample in samples
    ]

    with self.session() as sess:
      new_repr_ds = repr_dataset.replace_tensors_by_numpy_ndarrays(
          repr_ds, sess
      )

      # The resulting dataset should not contain any tf.Tensors.
      self.assertFalse(any(map(_contains_tensor, new_repr_ds)))

    # numpy ndarray types to be compatible with `make_tensor_proto`.
    if not context.executing_eagerly():
      with session.Session() as sess:
        repr_ds = replace_tensors_by_numpy_ndarrays(repr_ds, sess)

    expected_input_keys = self.expected_input_key_map.get(
        signature_def_key, None
    )
    tfrecord_file_path = self.path_map[signature_def_key]

      # Replaces the dataset with a new dataset where tf.Tensors are replaced
      # by their evaluated values.
      ds = repr_ds_map[signature_def_key]
      repr_ds_map[signature_def_key] = rd.replace_tensors_by_numpy_ndarrays(
          ds, sess
      )


def _create_sample_validator(
    expected_input_keys: Collection[str],
) -> Callable[[rd.RepresentativeSample], rd.RepresentativeSample]: