          if self._args.mask_type not in ("none", "predicted"):
              mask_centering = detected_face.mask[self._args.mask_type].stored_centering
          else:
              mask_centering = "face"  # Unused but requires a valid value
          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")
          new_face = np.concatenate((new_face, mask), -1)
          logger.trace("Got mask. Image shape: %s", new_face.shape)
          return new_face, raw_mask
  
      def _post_warp_adjustments(self, background, new_image):
 
</s> remove         logger.trace("Getting mask. Image shape: %s", new_face.shape)
 </s> add         logger.trace("Getting mask. Image 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         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
        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
          if self._args.mask_type not in ("none", "predicted"):
              mask_centering = detected_face.mask[self._args.mask_type].stored_centering
          else:
              mask_centering = "face"  # Unused but requires a valid value
        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
        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
        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
        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
        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
        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
          new_face = np.concatenate((new_face, mask), -1)
          logger.trace("Got mask. Image shape: %s", new_face.shape)
          return new_face, raw_mask
  
      def _post_warp_adjustments(self, background, new_image):
keep keep keep keep replace 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