const bool force_graph_mode_calibration,
      const absl::flat_hash_map<std::string, RepresentativeDatasetFile>&
          representative_dataset_file_map) const override {
    PYBIND11_OVERRIDE_PURE(std::optional<bool>, PyFunctionLibrary,
                           run_calibration, saved_model_path, signature_keys,
                           tags, force_graph_mode_calibration,

  # LINT.ThenChange()

  # LINT.IfChange(run_calibration)
  def run_calibration(
      self,
      saved_model_path: str,
      signature_keys: list[str],
      tags: set[str],
      force_graph_mode_calibration: bool,
      # Value type: RepresentativeDatasetFile.
      representative_dataset_file_map_serialized: dict[str, bytes],
  ) -> Optional[bool]: ...
  # LINT.ThenChange()

def _run_graph_for_calibration(
    float_model_dir: str,
    signature_keys: Sequence[str],
    tags: Collection[str],
    representative_dataset: rd.RepresentativeDatasetOrMapping,
    force_graph_mode_calibration: bool,
) -> None:
  """Runs the graph for calibration using representative datasets.

  Args:
    float_model_dir: Path to the model to calibrate.

  virtual std::optional<bool> RunCalibration(
      absl::string_view saved_model_path,
      const std::vector<std::string>& signature_keys,
      const std::unordered_set<std::string>& tags,
      bool force_graph_mode_calibration,
      const absl::flat_hash_map<std::string, RepresentativeDatasetFile>&
          representative_dataset_file_map) const = 0;
  // LINT.ThenChange(
  //     pywrap_function_lib.pyi:run_calibration,

    // Run calibration on the exported model.
    if (py_function_lib_->RunCalibration(
            calibration_saved_model_dir, signature_keys_, tags_,
            /*force_graph_mode_calibration=*/true,
            representative_dataset_file_map) == std::nullopt) {
      return absl::InternalError(
          "CalibrationComponent error: Failed to run calibration.");
    }
  }

    )

  # Set `force_graph_mode_calibration` to True to avoid skipping op execution,
  # which are not connected to return ops, during calibration execution.
  # TODO: b/335031954 - Bring back support to run calibration in Eager mode.
  logging.debug(
      'Setting `force_graph_mode_calibration = True` to ensure the calibration'
      ' mode is executed properly.'
  )

  // 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.
  bool force_graph_mode_calibration = 14;

  // Defines calibration options for quantization. This option is only used for
  // activation of static range quantization (SRQ). Quantization calibration
  // method is set to MIN_MAX by default.

        saved_model_path, tags, signature_def_map);

    py_function_library.RunCalibration(
        calibration_saved_model_dir, signature_keys, tags,
        quantization_options.force_graph_mode_calibration(),
        representative_dataset_file_map_serialized);
  }

  if (absl::Status status = AddCalibrationStatistics(
          *module_ref, calibration_data_dir,

        quantization_method=quant_opts_pb2.QuantizationMethod(
            preset_method=_PresetMethod.METHOD_STATIC_RANGE_INT8
        ),
        op_set=quant_opts_pb2.TF,
        force_graph_mode_calibration=True,
    )

    with self.assertLogs(level='INFO') as info_logs:
      # Save the logger verbosity.
      prev_log_level = logging.get_verbosity()
      logging.set_verbosity(logging.INFO)