// Lifts the quantizable spots as composite functions.
std::unique_ptr<OperationPass<ModuleOp>>
CreateLiftQuantizableSpotsAsFunctionsPass(
    const tensorflow::quantization::QuantizationOptions& quant_options);

// Apply graph optimizations such as fusing and constant folding to prepare
// lifting.
std::unique_ptr<OperationPass<func::FuncOp>> CreatePrepareLiftingPass(

    return true;
  return false;
}

std::optional<tensorflow::quantization::QuantizationComponentSpec>
GetWeightComponentSpec(
    const tensorflow::quantization::QuantizationOptions& quantization_options) {
  for (auto& cur_spec : quantization_options.quantization_method()
                            .quantization_component_specs()) {
    if (cur_spec.quantization_component() ==

// None, the default configuration is "do not quantize the model".
// 2) A set of supported operations.
// 3) Unit wise quantization precision.
// 4) Target hardware name.
// NEXT ID: 18
message QuantizationOptions {
  // The default quantization configuration for the model. If the below
  // unit-wise configuration does not exist, we use this quantization
  // configuration for the entire model. For each method, default configuration

      // The default minimum number of elements a weights array must have to be
      // quantized by this transformation.
      const int kWeightsMinNumElementsDefault = 1024;

      quantization::QuantizationOptions quantization_options;

      quantization_options.mutable_quantization_method()->set_preset_method(
          quantization::QuantizationMethod::METHOD_DYNAMIC_RANGE_INT8);