def _run_static_range_qat(
    src_saved_model_path: str,
    dst_saved_model_path: str,
    quant_opts: _QuantizationOptions,
    signature_def_map: _SignatureDefMap,
) -> None:
  """Runs static-range quantization for a Quantization-Aware Trained model.

  Runs the quantization for a model trained using QAT.

  Args:
    src_saved_model_path: Path to the source SavedModel directory.

from tensorflow.compiler.mlir.quantization.tensorflow.python import representative_dataset as rd

# LINT.IfChange(static_range_ptq)
def static_range_ptq(
    src_saved_model_path: str,
    dst_saved_model_path: str,
    quantization_config_serialized: bytes,
    *,
    signature_keys: list[str],
    signature_def_map_serialized: dict[str, bytes],

absl::Status PywrapQuantizeStaticRangePtq(
    absl::string_view src_saved_model_path,
    absl::string_view dst_saved_model_path, const QuantizationConfig& config,
    const std::vector<std::string>& signature_keys,
    const absl::flat_hash_map<std::string, SignatureDef>& signature_def_map,
    const PyFunctionLibrary& py_function_library) {
  return QuantizeStaticRangePtq(src_saved_model_path, dst_saved_model_path,

      const absl::string_view src_saved_model_path,
      const std::unordered_set<std::string>& tags,
      const absl::flat_hash_map<std::string, SignatureDef>& signature_def_map)
      const override {
    PYBIND11_OVERRIDE_PURE(std::optional<bool>, PyFunctionLibrary,
                           save_exported_model, dst_saved_model_path,
                           exported_model, src_saved_model_path, tags,

// on member fields.
class PyFunctionLibrary {
 public:
  virtual ~PyFunctionLibrary() = default;

  // Saves `exported_model` to `dst_saved_model_path` as SavedModel.
  // `src_saved_model_path` is the path to the source SavedModel from which the
  // exported model is produced. It is used to copy the asset files to
  // `dst_saved_model_path`. `tags` will be attached to the saved

  StaticRangePtqComponent(
      absl::Nonnull<MLIRContext*> ctx,
      absl::Nonnull<const tensorflow::quantization::PyFunctionLibrary*>
          py_function_library,
      absl::string_view src_saved_model_path,
      std::vector<std::string> signature_keys,
      std::unordered_set<std::string> tags,
      absl::flat_hash_map<std::string, tensorflow::SignatureDef>
          signature_def_map,

      const ::stablehlo::quantization::QuantizationConfig& config) override;

 private:
  absl::Nonnull<MLIRContext*> ctx_;
};

// Runs weight-only quantization on a SavedModel at
// `src_saved_model_path` and saves the resulting model to
// `dst_saved_model_path`.
//
// `quantization_config` configures the quantization behavior for the
// weight-only quantization.
//

class PyFunctionLibrary:

  # LINT.IfChange(save_exported_model)
  def save_exported_model(
      self,
      dst_saved_model_path: str,
      exported_model_serialized: bytes,
      src_saved_model_path: str,
      tags: set[str],
      serialized_signature_def_map: dict[str, bytes],
  ) -> Optional[bool]: ...
  # LINT.ThenChange()

  # LINT.IfChange(run_calibration)
  def run_calibration(
      self,

    exported_model: exported_model_pb2.ExportedModel,
    src_saved_model_path: str,
    dst_saved_model_path: str,
    signature_def_map: Mapping[str, meta_graph_pb2.SignatureDef],
    tags: Collection[str],
) -> bool:
  """Saves the model and copies the assets from the source model.

  Args:
    exported_model: ExportedModel to save.
    src_saved_model_path: Path to the source SavedModel. This will be used to