self.assertAllClose(expected_outputs, got_outputs, atol=1e-1)

    output_loader = saved_model_loader.SavedModelLoader(
        self._output_saved_model_path
    )
    output_graphdef = output_loader.get_meta_graph_def_from_tags(tags).graph_def
    self.assertTrue(self._contains_quantized_function_call(output_graphdef))

    # Check the converted model in the XLA opset.
    quantization_options = quant_opts_pb2.QuantizationOptions(

    A map from signature_key to its SignatureDef.
  """
  if tags is None:
    tags = {tag_constants.SERVING}

  loader = saved_model_loader.SavedModelLoader(saved_model_path)
  meta_graphdef = loader.get_meta_graph_def_from_tags(tags)
  signatures = {}
  for key, signature_def in meta_graphdef.signature_def.items():
    if key == saved_model_constants.INIT_OP_SIGNATURE_KEY:
      continue

  ) -> dict[str, str]:
    """Gets the function aliases in the output saved model."""
    loader = loader_impl.SavedModelLoader(output_saved_model_path)
    return loader.get_meta_graph_def_from_tags(
        tags
    ).meta_info_def.function_aliases

  def _create_matmul_model(
      self,
      input_shape: Sequence[int],
      weight_shape: Sequence[int],
      saved_model_path: str,