      distance: float:
          Filters out faces that are further than this distance from an "average" face. Set to
          ``0`` for off.
      """
      def __init__(self, min_scale: float, max_scale: float, distance: float):
          logger.debug("Initializing %s: (min_scale: %s, max_scale: %s, distance: %s)",
                       self.__class__.__name__, min_scale, max_scale, distance)
          self._min_scale = min_scale
          self._max_scale = max_scale
          self._distance = distance / 100.
          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)
 
</s> remove     distance: float:
 </s> add     distance: float </s> remove     def __call__(self, faces: List[DetectedFace], minimum_dimension: int) -> List[DetectedFace]:
 </s> add     def __call__(self, faces: List[DetectedFace], minimum_dimension: int
                 ) -> Tuple[List[DetectedFace], List[Optional[str]]]: </s> remove         for face in faces:
 </s> add         for idx, face in enumerate(faces):
    def __init__(self,
                 min_scale: float,
                 max_scale: float,
                 distance: float,
                 save_output: bool) -> None:
        logger.debug("Initializing %s: (min_scale: %s, max_scale: %s, distance: %s, "
                     "save_output: %s)", self.__class__.__name__, min_scale, max_scale, distance,
                     save_output)
      distance: float:
          Filters out faces that are further than this distance from an "average" face. Set to
          ``0`` for off.
      """
    def __init__(self,
                 min_scale: float,
                 max_scale: float,
                 distance: float,
                 save_output: bool) -> None:
        logger.debug("Initializing %s: (min_scale: %s, max_scale: %s, distance: %s, "
                     "save_output: %s)", self.__class__.__name__, min_scale, max_scale, distance,
                     save_output)
    def __init__(self,
                 min_scale: float,
                 max_scale: float,
                 distance: float,
                 save_output: bool) -> None:
        logger.debug("Initializing %s: (min_scale: %s, max_scale: %s, distance: %s, "
                     "save_output: %s)", self.__class__.__name__, min_scale, max_scale, distance,
                     save_output)
    def __init__(self,
                 min_scale: float,
                 max_scale: float,
                 distance: float,
                 save_output: bool) -> None:
        logger.debug("Initializing %s: (min_scale: %s, max_scale: %s, distance: %s, "
                     "save_output: %s)", self.__class__.__name__, min_scale, max_scale, distance,
                     save_output)
          self._min_scale = min_scale
          self._max_scale = max_scale
          self._distance = distance / 100.
          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)
keep keep keep keep replace replace replace keep keep keep keep keep
 Extract filter: Allow saving of filtered images 
plugins/extract/align/_base.py