"the `min_num_elements_for_weights`.");
      }
    }

    // Disable quantization if the quantization method is NO_QUANTIZE.
    QuantizationMethod quantization_method =
        quant_options_.quantization_method();
    if (quantization_method.quantization_component_specs().empty()) {
      return absl::InternalError(
          "The quantization method has been set to METHOD_NO_QUANTIZE.");
    }

// Defines arious options to specify and control the behavior of the
// StableHLO quantizer.
// NEXT ID: 2
message QuantizationOptions {
  QuantizationMethod quantization_method = 1;
}

// NEXT ID: 3
message QuantizationMethod {
  // Quantization Method can be either preset or custom.
  oneof quantization_method {
    PresetQuantizationMethod preset_quantization_method = 1;
    CustomQuantizationMethod custom_quantization_method = 2;
  }
}

// Lifts the dynamic range quantizable spots as composite functions.
std::unique_ptr<OperationPass<ModuleOp>>
CreateLiftQuantizableSpotsAsFunctionsDRQPass(
    tensorflow::quantization::QuantizationMethod::PresetMethod
        quantization_method,
    tensorflow::quantization::OpSet op_set, int min_num_elements_for_weights);

// Replaces tf.CustomAggregator ops with quant.Stats ops for finalizing the
// calibration procedure.

        root, temp_path, signatures=root.add.get_concrete_function()
    )

    quantization_options = quant_opts_pb2.QuantizationOptions(
        quantization_method=quant_opts_pb2.QuantizationMethod(
            preset_method=quant_opts_pb2.QuantizationMethod.PresetMethod.METHOD_STATIC_RANGE_INT8
        ),
        tags={tag_constants.SERVING},
        signature_keys=['serving_default'],
    )

  // the quantization configuration for units that are not specified in
  // unit-wise configurations.
  QuantizationMethod quantization_method = 1;

  OpSet op_set = 2;  // If not specified, it defaults to `XLA`.

  // Quantization spec for each unit. This quantization spec will override the
  // `quantization_method` for matching nodes and ops. If there are conflicts

                               QuantMethod quantization_method)
      : quant_specs_(quant_specs) {
    quant_specs_.inference_type = tensorflow::DT_QINT8;
    enable_per_channel_quantization_ = !quant_specs_.disable_per_channel;
    enable_post_training_quantize_ =
        (quantization_method == tensorflow::quantization::QuantizationMethod::
                                    METHOD_STATIC_RANGE_INT8);
  }

  LogicalResult match(func::FuncOp entry_func_op,
                      const Method& quantization_method) const override {
    if (!quantization_method.has_static_range_ptq()) {
      return failure();
    }
    return MatchGemmStyleOp<DotGeneralOp>(entry_func_op);
  }

  void rewrite(func::FuncOp entry_func_op, const Method& quantization_method,
               PatternRewriter& rewriter) const override {

  pm.addPass(mlir::quant::CreateInsertQuantizedFunctionsPass(
      quantization_options.quantization_method().preset_method(),
      quantization_options.op_set()));
  // TODO: b/260677670 - Pass quantization options as pass's inputs where
  // applicable
  pm.addPass(mlir::quant::CreateQuantizeCompositeFunctionsPass(
      quantization_options.quantization_method().preset_method(),
      quantization_options.op_set(),

                           const QuantizationOptions& quantization_options) {
  QuantizationOptions quantization_options_ = quantization_options;
  if (quantization_options.quantization_method()
          .has_preset_quantization_method()) {
    quantization_options_ =
        mlir::quant::stablehlo::FillPresetQuantizationOptions(
            quantization_options);
  }

  CustomQuantizationMethod custom_method =
      quantization_options_.quantization_method().custom_quantization_method();
  QuantizationComponentSpec *activation_component, *weight_component,
      *bias_component;
  const auto preset_method = quantization_options_.quantization_method()
                                 .preset_quantization_method()
                                 .preset_method();