              new_face = self._adjustments["seamless"].run(old_face, new_face, raw_mask)
          logger.trace("returning: new_face shape %s", new_face.shape)
          return new_face
  
      def _get_image_mask(self, new_face, detected_face, predicted_mask, reference_face):
          """ Return any selected image mask
  
          Places the requested mask into the new face's Alpha channel.
  
          Parameters
 
</s> remove         if self._adjustments["color"] is not None:
            new_face = self._adjustments["color"].run(old_face, new_face, raw_mask)
        if self._adjustments["seamless"] is not None:
            new_face = self._adjustments["seamless"].run(old_face, new_face, raw_mask)
        logger.trace("returning: new_face shape %s", new_face.shape)
 </s> add         if self._adjustments.color is not None:
            new_face = self._adjustments.color.run(old_face, new_face, raw_mask)
        if self._adjustments.seamless is not None:
            new_face = self._adjustments.seamless.run(old_face, new_face, raw_mask)
        logger.trace("returning: new_face shape %s", new_face.shape)  # type: ignore </s> remove         logger.trace("Got mask. Image shape: %s", new_face.shape)
 </s> add         logger.trace("Got mask. Image shape: %s", new_face.shape)  # type: ignore </s> remove     def _post_warp_adjustments(self, background, new_image):
 </s> add     def _post_warp_adjustments(self, background: np.ndarray, new_image: np.ndarray) -> np.ndarray: </s> remove     @staticmethod
    def process(old_face, new_face, raw_mask):
 </s> add     def process(self, old_face, new_face, raw_mask): </s> remove         for new_face, detected_face, reference_face in zip(predicted["swapped_faces"],
                                                           predicted["detected_faces"],
                                                           predicted["reference_faces"]):
 </s> add         for new_face, detected_face, reference_face in zip(predicted.swapped_faces,
                                                           predicted.inbound.detected_faces,
                                                           predicted.reference_faces):
    def _get_image_mask(self,
                        new_face: np.ndarray,
                        detected_face: "DetectedFace",
                        predicted_mask: Optional[np.ndarray],
                        reference_face: "AlignedFace") -> Tuple[np.ndarray, np.ndarray]:
              new_face = self._adjustments["seamless"].run(old_face, new_face, raw_mask)
          logger.trace("returning: new_face shape %s", new_face.shape)
          return new_face
  
    def _get_image_mask(self,
                        new_face: np.ndarray,
                        detected_face: "DetectedFace",
                        predicted_mask: Optional[np.ndarray],
                        reference_face: "AlignedFace") -> Tuple[np.ndarray, np.ndarray]:
          """ Return any selected image mask
  
          Places the requested mask into the new face's Alpha channel.
  
          Parameters
keep keep keep keep replace keep keep keep keep keep
 Bugfix: convert - Gif Writer
  - Fix non-launch error on Gif Writer
  - convert plugins - linting
  - convert/fs_media/preview/queue_manager - typing
  - Change convert items from dict to Dataclass 
lib/convert.py