using mlir::lite::CustomOpMap;
using mlir::lite::QuantizeWeights;
constexpr bool kUseUpdatedHybridSchemeDefault = true;

std::unique_ptr<ModelT> CreateMutableModelFromFile(const Model* input_model) {
  auto copied_model = std::make_unique<ModelT>();
  input_model->UnPackTo(copied_model.get(), nullptr);
  return copied_model;
}

std::unique_ptr<FlatBufferModel> ReadTestModel() {
  auto model_path = tensorflow::io::JoinPath(

}

TF_CAPI_EXPORT extern int TFE_OpGetInputLength(TFE_Op* op,
                                               const char* input_name,
                                               TF_Status* status) {
  int ret = -1;
  status->status = tensorflow::unwrap(op)->InputLength(input_name, &ret);
  return ret;
}

TF_CAPI_EXPORT extern int TFE_OpGetOutputLength(TFE_Op* op,

				label.TopologySubzone.Name: "subzone1",
			},
		},
	}

	controller, _ := NewFakeControllerWithOptions(t, FakeControllerOptions{})
	addNodes(t, controller, inputNode)

	assert.Equal(t, expectNode, controller.nodes.Get(inputNode.Name, ""))
}

func TestStripPodUnusedFields(t *testing.T) {
	inputPod := &corev1.Pod{
		TypeMeta: metav1.TypeMeta{
			Kind:       "Pod",
			APIVersion: "v1",

  // Retrieve the regression signature from meta graph def.
  const auto signature_def_map = metagraph_def.signature_def();
  const auto signature_def = signature_def_map.at("regress_x_to_y");

  const string input_name =
      signature_def.inputs().at(tensorflow::kRegressInputs).name();
  const string output_name =
      signature_def.outputs().at(tensorflow::kRegressOutputs).name();