          :class:`numpy.ndarray`
              The face output from the Faceswap Model with any requested pre-warp adjustments
              performed.
          """
          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
          new_face, raw_mask = self._get_image_mask(new_face,
                                                    detected_face,
                                                    predicted_mask,
                                                    reference_face)
          if self._adjustments["color"] is not None:
 
</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         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         if self._writer_pre_encode is not None:
            patched_face = self._writer_pre_encode(patched_face)
        logger.trace("Patched image: '%s'", predicted["filename"])
        return patched_face
 </s> add         if self._writer_pre_encode is None:
            retval: Union[np.ndarray, List[bytes]] = patched_face
        else:
            retval = self._writer_pre_encode(patched_face)
        logger.trace("Patched image: '%s'", predicted.inbound.filename)  # type: ignore
        return retval </s> remove         if self._adjustments["sharpening"] is not None:
            new_image = self._adjustments["sharpening"].run(new_image)
 </s> add         if self._adjustments.sharpening is not None:
            new_image = self._adjustments.sharpening.run(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
        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
          :class:`numpy.ndarray`
              The face output from the Faceswap Model with any requested pre-warp adjustments
              performed.
          """
        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
        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
        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
          new_face, raw_mask = self._get_image_mask(new_face,
                                                    detected_face,
                                                    predicted_mask,
                                                    reference_face)
          if self._adjustments["color"] is not None:
keep keep keep keep 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