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",

  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);

        ),
        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(

  QuantizationMethod quantization_method = 6;

  reserved 1 to 4;
}

// List of supported opsets to deploy the quantized model.
// The quantized model contains different set of ops depending on the opset.
// NEXT ID: 5
enum OpSet {
  OP_SET_UNSPECIFIED = 0;  // go/do-include-enum-unspecified
  // Uses TF ops that mimic quantization behavior. Used when the corresponding

QuantizedType CalculateUniformQuantParams(
    PatternRewriter& rewriter, TF::ConstOp op,
    tensorflow::quantization::QuantizationComponentSpec& weight_spec) {
  // TODO - b/278949920: Enable Per-Channel Quantization for XLA Opset
  // Currently, support symmetric, per-tensor, signed int8
  const bool kIsNarrowRange = true;
  const bool kIsSigned = true;
  const int kBitWidth = 8;

  DenseFPElementsAttr attr;

// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions='quantization-method=drq target-opset=UNIFORM_QUANTIZED' -quant-quantize-composite-functions='quantization-method=drq target-opset=UNIFORM_QUANTIZED' -symbol-dce | FileCheck %s

module {
  // TODO(b/260020937): Support transpose_a, transpose_b for matmul.
  func.func @matmul(%arg0: tensor<2x12xf32>) -> (tensor<*xf32>) {