std::unique_ptr<OperationPass<ModuleOp>> CreateQuantizeCompositeFunctionsPass(
    tensorflow::quantization::QuantizationMethod::PresetMethod
        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);

        ),
        _DYNAMIC_RANGE_DEFAULT_MIN_NUM_ELEMENTS_FOR_WEIGHTS,
    )

  if not quantization_options.HasField('enable_per_channel_quantization'):
    quantization_options.enable_per_channel_quantization = False

  if quantization_options.enable_per_channel_quantization and not (
      (
          quantization_options.op_set == quant_opts_pb2.OpSet.UNIFORM_QUANTIZED

  // not applied regardless of the op support. Currently, it is supported for
  // XLA opset for SRQ on weight tensors (not activation),
  // and Uniform Quantized opset .
  optional bool enable_per_channel_quantization = 10;

  // Enables two inputs of an operation to be both tensors.
  // Currently supports MatMul and BatchMatMul ops for XLA.
  // TODO(b/263528090): Check the condition when this feature is beneficial.

  // TODO: b/331302857 - Remove `enable_per_channel_quantized_weight` usage.
  quantization_config.mutable_static_range_ptq_preset()
      ->set_enable_per_channel_quantized_weight(
          quantization_options.enable_per_channel_quantization());
  // When targeting server TPUs quantized types should be unpacked into
  // integer ops.
  quantization_config.mutable_pipeline_config()->set_unpack_quantized_types(
      true);