// accumulation over the given input type.
Type GetSumAccumulationType(Type input_type) {
  MLIRContext *ctx = input_type.getContext();
  if (input_type.isBF16() || input_type.isF16()) return FloatType::getF32(ctx);
  if (input_type.isSignlessInteger(8) || input_type.isSignlessInteger(16))
    return IntegerType::get(ctx, 32);
  return input_type;
}

// Returns axis in HLO format from TF elements attr with exactly one element or

  for (auto input_type : func_type.getInputs()) {
    tensorflow::TensorShape tensor_shape;
    xla::Shape xla_shape = xla::TypeToShape(input_type);
    TF_RETURN_IF_ERROR(tensorflow::TensorShape::BuildTensorShape(
        xla_shape.dimensions(), &tensor_shape));
    arg_shapes.emplace_back(tensor_shape);

    DataType dtype;
    TF_RETURN_IF_ERROR(ConvertToDataType(input_type, &dtype));

// Quantizes the input model represented as `model_buffer` and writes the result
// to the `output_buffer`. Both `model_buffer` and `output_buffer` should be a
// valid FlatBuffer format for Model supported by TFLite.
//
// The `input_type`, `output_type` and `inference_type` can be float32 / qint8 /
// int8 / int16.
//
// Returns a partially quantized model if `fully_quantize` is false. Returns a
// non-OK status if the quantization fails.
//

               << ", input_inference_type: "
               << tflite::EnumNameTensorType(input_type)
               << ", output_inference_type: "
               << tflite::EnumNameTensorType(output_type) << "\n";
  mlir::Builder mlir_builder(&context);
  mlir::Type input_mlir_type =
      tflite::ConvertElementType(input_type, mlir_builder);
  mlir::Type output_mlir_type =

TfLiteStatus QuantizeModel(ModelT* model, const TensorType& input_type,
                           const TensorType& output_type, bool allow_float,
                           std::string& output_buffer) {
  return QuantizeModel(model, input_type, output_type, allow_float,
                       /*operator_names=*/{}, TensorType_INT8, output_buffer);
}

TfLiteStatus QuantizeModel(ModelT* model, const TensorType& input_type,

  const tflite::TensorType input_type =
      FromTocoDataTypeToTflitToTensorType(input_data_type);
  const tflite::TensorType output_type =
      FromTocoDataTypeToTflitToTensorType(output_data_type);

  std::string output_model;
  const absl::string_view input_model_buffer(buf, length);
  auto status = mlir::lite::QuantizeModel(
      input_model_buffer, input_type, output_type, inference_tensor_type,

	11, // 20: istio.workload.Workload.ServicesEntry.value:type_name -> istio.workload.PortList
	21, // [21:21] is the sub-list for method output_type
	21, // [21:21] is the sub-list for method input_type
	21, // [21:21] is the sub-list for extension type_name
	21, // [21:21] is the sub-list for extension extendee
	0,  // [0:21] is the sub-list for field type_name
}

    ArrayRef<int64_t> shape = arg_shapes[i];
    Type element_type;
    if (auto input_ty =
            mlir::dyn_cast<RankedTensorType>(func_type.getInput(i))) {
      if (input_ty.getRank() != shape.size()) {
        return failure();
      }
      element_type = input_ty.getElementType();
    } else {
      auto unranked_input_ty =

      enable_shape_inference, unconditionally_use_set_output_shapes,
      enable_soft_placement,  set_original_tf_func_name};

  auto module_or = tensorflow::GraphdefToMlirTranslateFunction(
      input, input_arrays, input_dtypes, input_shapes, output_arrays,
      control_output_arrays, options, context);
  if (!module_or.status().ok()) return nullptr;
  return std::move(module_or).value();
}

    }

    static String convertingValueSourceImpl(String valueSourceClassName, String inputType, String returnType, String conversion) {
        """
        abstract class $valueSourceClassName implements ${ValueSource.name}<$returnType, Params> {
            interface Params extends ${ValueSourceParameters.name} {
                Property<$inputType> getInput()
            }

            @Override $returnType obtain() {