  
          logger.trace("Final shape: %s", retval.shape)  # type:ignore
          return retval
  
      def _queue_out_frames(self, batch: List[ConvertItem], swapped_faces: np.ndarray) -> None:
          """ Compile the batch back to original frames and put to the Out Queue.
  
          For batching, faces are split away from their frames. This compiles all detected faces
          back to their parent frame before putting each frame to the out queue in batches.
  
 
</s> remove     def _process_batch(self, batch: List[ConvertItem], faces_seen: int):
 </s> add     def _process_batch(self, batch: T.List[ConvertItem], faces_seen: int): </s> remove         input_ = [cast(np.ndarray, feed.face)[..., :3] for feed in batch.feed_faces]
 </s> add         input_ = [T.cast(np.ndarray, feed.face)[..., :3] for feed in batch.feed_faces] </s> remove         input_ = np.array([cast(np.ndarray, feed.face)[..., :3]
 </s> add         input_ = np.array([T.cast(np.ndarray, feed.face)[..., :3] </s> remove     def _detect_faces(self, filename: str, image: np.ndarray) -> List[DetectedFace]:
 </s> add     def _detect_faces(self, filename: str, image: np.ndarray) -> T.List[DetectedFace]: </s> remove         retval = np.stack([cast(np.ndarray, feed_face.face)[..., :3]
 </s> add         retval = np.stack([T.cast(np.ndarray, feed_face.face)[..., :3]
    def _queue_out_frames(self, batch: T.List[ConvertItem], swapped_faces: np.ndarray) -> None:
  
          logger.trace("Final shape: %s", retval.shape)  # type:ignore
          return retval
  
    def _queue_out_frames(self, batch: T.List[ConvertItem], swapped_faces: np.ndarray) -> None:
          """ Compile the batch back to original frames and put to the Out Queue.
  
          For batching, faces are split away from their frames. This compiles all detected faces
          back to their parent frame before putting each frame to the out queue in batches.
  
keep keep keep keep replace keep keep keep keep keep
 Remove plaidML support (#1325)

* Remove PlaidML reference from readme files

* Remove AMD option from installers

* remove amd requirements and update setup.py

* remove plaidml test from CI workflow

* gpustats: remove plaidml backend

* plaid removals:
  - faceswap.py - python version check
  - setup.cfg - plaidml typing ignore
  - lib.keras_utils - All plaid code
  - lib.launcher.py - All plaidml checks and configuration

* remove tf2.2 specific code from GUI event reader

* lib.model - remove all plaidml implementations

* plugins.extract - remove plaidml code

* plugins.train remove plaidml code

* lib.convert - remove plaidml code

* tools.model: remove plaidml code

* Remove plaidML tests from unit tests

* remove plaidml_utils and docsting cleanups

* Remove plaidML refs from configs

* fix keras imports 
scripts/convert.py