tf_module = module_ctor()
  if version == 2:
    options = save_options.SaveOptions(save_debug_info=True)
    saved_model.save(tf_module, export_path, options=options)
  else:
    builder = saved_model.builder.SavedModelBuilder(export_path)
    builder.add_meta_graph_and_variables(tf_module, ["serve"])
    builder.save()


if __name__ == "__main__":
  v2_compat.enable_v2_behavior()

    signature_def_map, init_op, assets_collection = create_signature()

    sess = tf.Session()
    sess.run(tf.initializers.global_variables())
    builder = tf.saved_model.builder.SavedModelBuilder(save_model_path)
    builder.add_meta_graph_and_variables(
        sess,
        [tf.saved_model.tag_constants.SERVING],
        signature_def_map,
        main_op=init_op,

def _save_function_alias(
    saved_model_dir: str,
    tags: Collection[str],
    function_aliases: Mapping[str, str],
) -> None:
  """Saves the function alias to the SavedModel.

  SavedModelBuilder (TF1 saved model saver) does not support saving function
  aliases, so this function loads the SavedModel proto and adds the
  `function_aliases` field.

  Args:

  ):
    # Convert any non-Tensor values from the sample to Tensors.
    # This conversion is required because the model saved in `model_dir` is
    # saved using TF1 SavedModelBuilder, which doesn't save the
    # SavedObjectGraph.
    func_kwargs = _convert_values_to_tf_tensors(sample)
    func(**func_kwargs)


def _run_graph_for_calibration_eager_mode(
    model_dir: str,

      init_op: Op for initialization.
      assets_collection: Assets collection. This collection is a list of string
        tensors. Each tensor contains the asset file names.
    """
    v1_builder = builder.SavedModelBuilder(saved_model_path)
    sig_def = signature_def_utils_impl.predict_signature_def(
        inputs=inputs, outputs=outputs
    )

    v1_builder.add_meta_graph_and_variables(
        sess,
        tags,

          inputs=input_map, outputs=output_map
      )
      signature_map = {'main': signature}

      tags = {tag_constants.SERVING}
      v1_builder = builder.SavedModelBuilder(self._input_saved_model_path)
      v1_builder.add_meta_graph_and_variables(
          sess, tags, signature_def_map=signature_map
      )
      v1_builder.save()

    *   TFRecordWriter now raises an error if .write() fails.
    *   TPU: More helpful error messages in TPUClusterResolvers.
    *   The legacy_init_op argument to SavedModelBuilder methods for adding
        MetaGraphs has been deprecated. Please use the equivalent main_op
        argument instead. As part of this, we now explicitly check for a single