Option<OpSet> target_opset_{
      *this, "target-opset", llvm::cl::init(OpSet::TF),
      llvm::cl::desc("Choose target opset."),
      llvm::cl::values(
          clEnumValN(OpSet::TF, "TF",
                     "Uses TF ops that mimic quantization behavior"),
          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),
          clEnumValN(OpSet::UNIFORM_QUANTIZED, "UNIFORM_QUANTIZED",

  Option<OpSet> target_opset_{
      *this, "target-opset", llvm::cl::init(OpSet::TF),
      llvm::cl::desc("Choose target opset."),
      llvm::cl::values(
          clEnumValN(OpSet::TF, "TF",
                     "Uses TF ops that mimic quantization behavior"),
          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),
          clEnumValN(OpSet::UNIFORM_QUANTIZED, "UNIFORM_QUANTIZED",

 private:
  Option<OpSet> target_opset_{
      *this, "target-opset", llvm::cl::init(OpSet::TF),
      llvm::cl::desc("Choose target opset."),
      llvm::cl::values(
          clEnumValN(OpSet::TF, "TF",
                     "Uses TF ops that mimic quantization behavior"),
          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),
          clEnumValN(OpSet::UNIFORM_QUANTIZED, "UNIFORM_QUANTIZED",

			opset(AADDD, r0)
			opset(ASUBF, r0)
			opset(ASUBD, r0)
			opset(AMULF, r0)
			opset(AMULD, r0)
			opset(ANMULF, r0)
			opset(ANMULD, r0)
			opset(AMULAF, r0)
			opset(AMULAD, r0)
			opset(AMULSF, r0)
			opset(AMULSD, r0)
			opset(ANMULAF, r0)
			opset(ANMULAD, r0)
			opset(ANMULSF, r0)
			opset(ANMULSD, r0)
			opset(AFMULAF, r0)
			opset(AFMULAD, r0)
			opset(AFMULSF, r0)

  bool test_mode_;
  Option<OpSet> op_set_{
      *this, "target-opset", llvm::cl::init(OpSet::TF),
      llvm::cl::desc("Choose target opset."),
      llvm::cl::values(
          clEnumValN(OpSet::TF, "TF",
                     "Uses TF ops that mimic quantization behavior"),
          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),
          clEnumValN(OpSet::UNIFORM_QUANTIZED, "UNIFORM_QUANTIZED",

    const QuantizationSpecs& quant_specs,
    tensorflow::quantization::OpSet op_set);

// Creates an instance of the PreprocessOp pass, which will perform op
// preprocessing to allow multi-axis quantization, prior to quantization.
std::unique_ptr<OperationPass<ModuleOp>> CreatePreprocessOpPass(
    tensorflow::quantization::OpSet op_set,
    tensorflow::quantization::QuantizationMethod::PresetMethod
        quantization_method,

      (
          quantization_options.op_set == quant_opts_pb2.OpSet.UNIFORM_QUANTIZED
          or quantization_options.quantization_method.preset_method
          == _PresetMethod.METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8
      )
      or (
          quantization_options.op_set
          in (quant_opts_pb2.OpSet.XLA, quant_opts_pb2.OpSet.STABLEHLO)
          and quantization_options.quantization_method.preset_method

    const absl::flat_hash_map<std::string, std::string> &function_aliases,
    absl::string_view calibration_data_dir) {
  const bool is_stablehlo = quantization_options.op_set() == OpSet::STABLEHLO;
  // Use StableHLO Quantizer option if opset is specified.
  if (is_stablehlo) {
    const QuantizationConfig quantization_config =
        GetQuantizationConfigForStaticRangePtq(quantization_options);

// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions='quantization-method=weight_only target-opset=XLA' -quant-quantize-composite-functions='quantization-method=weight_only target-opset=XLA enable-per-channel-quantization=true' -symbol-dce | FileCheck --c...

        ),
        tags=tags,
        signature_keys=['serving_default'],
        op_set=target_opset,
    )

    if target_opset != quant_opts_pb2.XLA:
      # Uniform quantized opset is not supported for weight-only
      with self.assertRaisesRegex(
          ValueError, 'TF/Uniform quantized opset does not support weight-only.'
      ):
        converted_model = quantize_model.quantize(