          self._max_scale = max_scale
          self._distance = distance / 100.
          self._roll = roll
          self._save_output = save_output
          self._active = max_scale > 0.0 or min_scale > 0.0 or distance > 0.0
          self._counts: Dict[str, int] = dict(min_scale=0, max_scale=0, distance=0, roll=0)
          logger.debug("Initialized %s: ", self.__class__.__name__)
  
      def __call__(self, faces: List[DetectedFace], minimum_dimension: int
                   ) -> Tuple[List[DetectedFace], List[Optional[str]]]:
 
</s> remove                      "save_output: %s)", self.__class__.__name__, min_scale, max_scale, distance,
                     roll, save_output)
 </s> add                      "save_output: %s, disable: %s)", self.__class__.__name__, min_scale,
                     max_scale, distance, roll, save_output, disable) </s> remove                  save_output: bool) -> None:
 </s> add                  save_output: bool,
                 disable: bool = False) -> None: </s> remove                                      save_output=self.config["save_filtered"])
 </s> add                                      save_output=self.config["save_filtered"],
                                     disable=disable_filter) </s> remove                  re_feed: int = 0, **kwargs) -> None:
        logger.debug("Initializing %s: (normalize_method: %s, re_feed: %s)",
                     self.__class__.__name__, normalize_method, re_feed)
 </s> add                  re_feed: int = 0,
                 disable_filter: bool = False,
                 **kwargs) -> None:
        logger.debug("Initializing %s: (normalize_method: %s, re_feed: %s, disable_filter: %s)",
                     self.__class__.__name__, normalize_method, re_feed, disable_filter) </s> remove                  image_is_aligned: bool = False) -> None:
 </s> add                  disable_filter: bool = False,
                 image_is_aligned: bool = False,) -> None:
        self._active = not disable and (max_scale > 0.0 or min_scale > 0.0 or distance > 0.0)
          self._max_scale = max_scale
          self._distance = distance / 100.
          self._roll = roll
          self._save_output = save_output
        self._active = not disable and (max_scale > 0.0 or min_scale > 0.0 or distance > 0.0)
          self._counts: Dict[str, int] = dict(min_scale=0, max_scale=0, distance=0, roll=0)
          logger.debug("Initialized %s: ", self.__class__.__name__)
  
      def __call__(self, faces: List[DetectedFace], minimum_dimension: int
                   ) -> Tuple[List[DetectedFace], List[Optional[str]]]:
keep keep keep keep replace keep keep keep keep keep
 Bugfix: Manual tool. Explicitly disable aligner filters 
plugins/extract/align/_base.py