      def _get_mask(self,
                    detected_face: DetectedFace,
                    predicted_mask: Optional[np.ndarray],
                    centering: Literal["legacy", "face", "head"],
                    sub_crop_offset: Optional[np.ndarray]) -> np.ndarray:
          """ Return the requested mask with any requested blurring applied.
  
          Parameters
          ----------
          detected_face: :class:`lib.align.DetectedFace`
 
</s> remove                          sub_crop_offset: Optional[np.ndarray]) -> np.ndarray:
 </s> add                          source_offset: np.ndarray,
                         target_offset: np.ndarray) -> np.ndarray: </s> remove             sub_crop_offset: Optional[np.ndarray],
 </s> add             source_offset: np.ndarray,
            target_offset: np.ndarray, </s> remove         sub_crop_offset: :class:`numpy.ndarray`
            The (x, y) offset to crop the mask from the center point. Set to `None` if the mask
            does not need to be offset for alternative centering
 </s> add         source_offset: :class:`numpy.ndarray`
            The (x, y) offset for the mask at its stored centering
        target_offset: :class:`numpy.ndarray`
            The (x, y) offset for the mask at the requested target centering </s> remove         sub_crop_offset: :class:`numpy.ndarray`, optional
            The (x, y) offset to crop the mask from the center point.
 </s> add         source_offset: :class:`numpy.ndarray`
            The (x, y) offset for the mask at its stored centering
        target_offset: :class:`numpy.ndarray`
            The (x, y) offset for the mask at the requested target centering </s> remove             mask = self._get_stored_mask(detected_face, centering, sub_crop_offset)
 </s> add             mask = self._get_stored_mask(detected_face, centering, source_offset, target_offset)
                  source_offset: np.ndarray,
                  target_offset: np.ndarray) -> np.ndarray:
      def _get_mask(self,
                    detected_face: DetectedFace,
                    predicted_mask: Optional[np.ndarray],
                    centering: Literal["legacy", "face", "head"],
                  source_offset: np.ndarray,
                  target_offset: np.ndarray) -> np.ndarray:
          """ Return the requested mask with any requested blurring applied.
  
          Parameters
          ----------
          detected_face: :class:`lib.align.DetectedFace`
keep keep keep keep replace keep keep keep keep keep
 lib.detected_face.Mask
  - Add source + target offset and coverage to set_sub_crop method 
plugins/convert/mask/mask_blend.py