          new_face, raw_mask = self._get_image_mask(new_face,
                                                    detected_face,
                                                    predicted_mask,
                                                    reference_face)
          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)
          return new_face
  
      def _get_image_mask(self, new_face, detected_face, predicted_mask, reference_face):
          """ Return any selected image mask
  
 
</s> remove     def _get_image_mask(self, new_face, detected_face, predicted_mask, reference_face):
 </s> add     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]: </s> remove         logger.trace("new_face shape: %s, predicted_mask shape: %s", new_face.shape,
                     predicted_mask.shape if predicted_mask is not None else None)
        old_face = reference_face.face[..., :3] / 255.0
 </s> add         logger.trace("new_face shape: %s, predicted_mask shape: %s",  # type: ignore
                     new_face.shape, predicted_mask.shape if predicted_mask is not None else None)
        old_face = cast(np.ndarray, reference_face.face)[..., :3] / 255.0 </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): </s> remove         mask, raw_mask = self._adjustments["mask"].run(detected_face,
                                                       reference_face.pose.offset[mask_centering],
                                                       reference_face.pose.offset[self._centering],
                                                       self._centering,
                                                       predicted_mask=predicted_mask)
        logger.trace("Adding mask to alpha channel")
 </s> add         assert self._adjustments.mask is not None
        mask, raw_mask = self._adjustments.mask.run(detected_face,
                                                    reference_face.pose.offset[mask_centering],
                                                    reference_face.pose.offset[self._centering],
                                                    self._centering,
                                                    predicted_mask=predicted_mask)
        logger.trace("Adding mask to alpha channel")  # 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
        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
          new_face, raw_mask = self._get_image_mask(new_face,
                                                    detected_face,
                                                    predicted_mask,
                                                    reference_face)
        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
        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
        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
        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
        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
          return new_face
  
      def _get_image_mask(self, new_face, detected_face, predicted_mask, reference_face):
          """ Return any selected image mask
  
keep keep keep keep replace replace replace replace 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