tag_names: list[str] = ["serve"],
    input_arg_shapes_str: str = "",
) -> bytes: ...
# LINT.ThenChange()

# LINT.IfChange(tensorflow_module_to_stablehlo)
def tensorflow_module_to_stablehlo(
    module: str,
    input_arg_shapes_str: str = "",
) -> bytes: ...

        py::arg("quantization_config_serialized"), py::kw_only(),
        py::arg("signature_keys"), py::arg("signature_def_map_serialized"),
        py::arg("py_function_library"));
  // LINT.ThenChange(pywrap_quantization.pyi:static_range_ptq)

  // If the function signature changes, likely its corresponding .pyi type
  // hinting should also change.
  // LINT.IfChange(weight_only_ptq)

    py_function_library: py_function_lib.PyFunctionLibrary,
) -> Any: ...  # Status

# LINT.ThenChange()

# LINT.IfChange(populate_default_configs)
def populate_default_configs(
    user_provided_quantization_config_serialized: bytes,
) -> bytes: ...  # QuantizationConfig

# LINT.ThenChange()

# LINT.IfChange(expand_preset_configs)
def expand_preset_configs(
    quantization_config_serialized: bytes,

  ) -> Optional[bool]: ...
  # LINT.ThenChange()

  # LINT.IfChange(get_calibration_min_max_value)
  def get_calibration_min_max_value(
      self,
      calibration_statistics_serialized: bytes,
      calibration_options_serialized: bytes,
  ) -> Optional[tuple[float, float]]: ...

) -> Any: ...  # Status

# LINT.ThenChange()

# LINT.IfChange(quantize_weight_only)
def quantize_weight_only(
    src_saved_model_path: str,
    dst_saved_model_path: str,
    quantization_options_serialized: bytes,
    *,
    signature_def_map_serialized: dict[str, bytes],
    py_function_library: py_function_lib.PyFunctionLibrary,
) -> Any: ...  # Status

# LINT.ThenChange()

      absl::string_view src_saved_model_path,
      const std::unordered_set<std::string>& tags,
      const absl::flat_hash_map<std::string, tensorflow::SignatureDef>&
          signature_def_map) const = 0;
  // LINT.ThenChange(
  //     pywrap_function_lib.pyi:save_exported_model,
  //     py_function_lib.py:save_exported_model,
  // )

  // Runs calibration on a model saved at `saved_model_path`. `exported_model`

def clear_calibrator() -> None: ...

# LINT.ThenChange()

# LINT.IfChange(clear_data_from_calibrator)
def clear_data_from_calibrator(id: bytes) -> None: ...

# LINT.ThenChange()

# LINT.IfChange(get_statistics_from_calibrator)
def get_statistics_from_calibrator(
    id: bytes,
) -> calibration_statistics_pb2.CalibrationStatistics: ...

        readability-redundant-control-flow,
        readability-simplify-boolean-expr,
        readability-simplify-subscript-expr,
        readability-use-anyofallof
        # LINT.ThenChange(METADATA:checks)

CheckOptions:
  # LINT.IfChange(check-options)
  - key:             readability-identifier-naming.ClassCase
    value:           CamelCase
  - key:             readability-identifier-naming.EnumCase

         const absl::flat_hash_map<std::string, SignatureDef>&
             signature_def_map,
         const PyFunctionLibrary& py_function_library) -> absl::Status {
        // LINT.ThenChange(pywrap_quantize_model.pyi:quantize_qat_model)
        std::unordered_set<std::string> tags;
        tags.insert(quantization_options.tags().begin(),
                    quantization_options.tags().end());

      py::arg("exported_model_signatures") =
          std::vector<std::string>{"serving_default"},
      py::arg("tag_names") = std::vector<std::string>{"serve"},
      py::arg("input_arg_shapes_str") = "");
  // LINT.ThenChange(pywrap_tensorflow_to_stablehlo.pyi:savedmodel_to_stablehlo)
  //
  // LINT.IfChange(tensorflow_module_to_stablehlo)
  m.def(
      "tensorflow_module_to_stablehlo",
      [](absl::string_view module_op_str,