          placeholder = np.zeros((frame_size[1], frame_size[0], 4), dtype="float32")
          background = predicted["image"] / np.array(255.0, dtype="float32")
          placeholder[:, :, :3] = background
  
          for new_face, detected_face, reference_face in zip(predicted["swapped_faces"],
                                                             predicted["detected_faces"],
                                                             predicted["reference_faces"]):
              predicted_mask = new_face[:, :, -1] if new_face.shape[2] == 4 else None
              new_face = new_face[:, :, :3]
              interpolator = reference_face.interpolators[1]
  
              new_face = self._pre_warp_adjustments(new_face,
 
</s> remove         background = predicted["image"] / np.array(255.0, dtype="float32")
 </s> add         background = predicted.inbound.image / np.array(255.0, dtype="float32") </s> remove         logger.trace("Getting: (filename: '%s', faces: %s)",
                     predicted["filename"], len(predicted["swapped_faces"]))
 </s> add         logger.trace("Getting: (filename: '%s', faces: %s)",  # type: ignore
                     predicted.inbound.filename, len(predicted.swapped_faces)) </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         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             retval.append(cv2.imencode(self._extension,  # pylint: disable=no-member
 </s> add             retval.append(cv2.imencode(self._extension,
        for new_face, detected_face, reference_face in zip(predicted.swapped_faces,
                                                           predicted.inbound.detected_faces,
                                                           predicted.reference_faces):
          placeholder = np.zeros((frame_size[1], frame_size[0], 4), dtype="float32")
          background = predicted["image"] / np.array(255.0, dtype="float32")
          placeholder[:, :, :3] = background
  
        for new_face, detected_face, reference_face in zip(predicted.swapped_faces,
                                                           predicted.inbound.detected_faces,
                                                           predicted.reference_faces):
        for new_face, detected_face, reference_face in zip(predicted.swapped_faces,
                                                           predicted.inbound.detected_faces,
                                                           predicted.reference_faces):
        for new_face, detected_face, reference_face in zip(predicted.swapped_faces,
                                                           predicted.inbound.detected_faces,
                                                           predicted.reference_faces):
              predicted_mask = new_face[:, :, -1] if new_face.shape[2] == 4 else None
              new_face = new_face[:, :, :3]
              interpolator = reference_face.interpolators[1]
  
              new_face = self._pre_warp_adjustments(new_face,
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