quantization_options.op_set(),
      quantization_options.enable_per_channel_quantization(),
      quantization_options.min_num_elements_for_weights(),
      quantization_options.enable_legacy_weight_only(), mlir_dump_file_prefix));
  pm.addPass(mlir::createSymbolDCEPass());
  pm.addPass(mlir::TF::CreateTFShapeInferencePass());

  // TODO: b/264637396 - Deprecate TF opset

      const bool enable_per_channel_quantization,
      const int min_num_elements_for_weights,
      const bool enable_legacy_weight_only,
      std::optional<const std::string> mlir_dump_file_name)
      : enable_legacy_weight_only_(enable_legacy_weight_only),
        min_num_elements_for_weights_(min_num_elements_for_weights),
        mlir_dump_file_name_(std::move(mlir_dump_file_name)) {

  // Produces legacy weight-only graph where the qconst op(containing quantized
  // values) is followed by a dequantization op. This flag will be deprecated.
  bool enable_legacy_weight_only = 13;

  // If set to true, it forces calibration in graph model instead of eager mode
  // when the context is in eager mode. This will be forcibly set to true when
  // using DebuggerOptions.

        quantization_method,
    tensorflow::quantization::OpSet target_opset,
    bool enable_per_channel_quantization, int min_num_elements_for_weights,
    bool enable_legacy_weight_only = false,
    std::optional<const absl::string_view> mlir_dump_file_prefix =
        std::nullopt);

// Converts dequantize-(quantizable) call-quantize pattern to a single call op

    )

  if not quantization_options.HasField('freeze_all_variables'):
    quantization_options.freeze_all_variables = True

  if quantization_options.enable_legacy_weight_only:
    raise ValueError(
        'Legacy weight-only is deprecated. Use weight-only quantization method.'
    )

  # Converter assumes options are specified. So set SRQ explicitly.
  if (