          then puts the predictions back to the :attr:`self.out_queue`
          """
          faces_seen = 0
          consecutive_no_faces = 0
          batch: List[ConvertItem] = []
          while True:
              item: Union[Literal["EOF"], ConvertItem] = self._in_queue.get()
              if item == "EOF":
                  logger.debug("EOF Received")
                  if batch:  # Process out any remaining items
 
</s> remove             item: Union[Literal["EOF"], ConvertItem] = self._in_queue.get()
 </s> add             item: T.Union[Literal["EOF"], ConvertItem] = self._in_queue.get() </s> remove     def _queue_out_frames(self, batch: List[ConvertItem], swapped_faces: np.ndarray) -> None:
 </s> add     def _queue_out_frames(self, batch: T.List[ConvertItem], swapped_faces: np.ndarray) -> None: </s> remove         self._mixed_precision_layers: List[str] = []
 </s> add         self._mixed_precision_layers: T.List[str] = [] </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             if get_backend() == "amd" and feed_faces.shape[0] != self._batchsize:
                logger.verbose("Fallback to BS=1")  # type:ignore
                batch_size = 1
 </s> add 
        batch: T.List[ConvertItem] = []
          then puts the predictions back to the :attr:`self.out_queue`
          """
          faces_seen = 0
          consecutive_no_faces = 0
        batch: T.List[ConvertItem] = []
          while True:
              item: Union[Literal["EOF"], ConvertItem] = self._in_queue.get()
              if item == "EOF":
                  logger.debug("EOF Received")
                  if batch:  # Process out any remaining items
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