/*tfrt*/ ::testing::Values(false),
                       /*executing_eagerly*/ ::testing::Values(true, false)));
#else
INSTANTIATE_TEST_SUITE_P(
    UnifiedCAPI, CppGradients,
    ::testing::Combine(::testing::Values("graphdef", "mlir"),
                       /*tfrt*/ ::testing::Values(false),
                       /*executing_eagerly*/ ::testing::Values(true, false)));
#endif
}  // namespace

INSTANTIATE_TEST_SUITE_P(
    CustomGradientTest, CustomGradientTest,
    ::testing::Combine(::testing::Values("graphdef", "mlir"),
                       /*tfrt*/ ::testing::Values(false),
                       /*executing_eagerly*/ ::testing::Values(true, false)));

}  // namespace
}  // namespace internal
}  // namespace gradients

      the set of expected input keys.
    """
    # When running in graph mode (TF1), tf.Tensor types should be converted to
    # 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
    )

    # `signature_keys` is guaranteed to have only one element after the
    # validation.
    representative_dataset_map = {signature_keys[0]: representative_dataset}

  try:
    if context.executing_eagerly() and not force_graph_mode_calibration:
      logging.info('Calibration step is executed in eager mode.')
      _run_graph_for_calibration_eager_mode(
          float_model_dir, tags, representative_dataset_map
      )