          :class:`numpy.ndarray`
              A batch of faces ready for feeding into the Faceswap model.
          """
          logger.trace("Compiling feed face. Batchsize: %s", len(feed_faces))  # type:ignore
          retval = np.stack([cast(np.ndarray, feed_face.face)[..., :3]
                             for feed_face in feed_faces]) / 255.0
          logger.trace("Compiled Feed faces. Shape: %s", retval.shape)  # type:ignore
          return retval
  
      def _predict(self, feed_faces: np.ndarray, batch_size: Optional[int] = None) -> np.ndarray:
 
</s> remove     def _predict(self, feed_faces: np.ndarray, batch_size: Optional[int] = None) -> np.ndarray:
 </s> add     def _predict(self, feed_faces: np.ndarray, batch_size: T.Optional[int] = None) -> np.ndarray: </s> remove     def _compile_feed_faces(feed_faces: List[AlignedFace]) -> np.ndarray:
 </s> add     def _compile_feed_faces(feed_faces: T.List[AlignedFace]) -> np.ndarray: </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         batch.feed = ((np.array([cast(np.ndarray, feed.face)[..., :3]
 </s> add         batch.feed = ((np.array([T.cast(np.ndarray, feed.face)[..., :3] </s> remove         input_ = np.array([cast(np.ndarray, feed.face)[..., :3]
 </s> add         input_ = np.array([T.cast(np.ndarray, feed.face)[..., :3]
        retval = np.stack([T.cast(np.ndarray, feed_face.face)[..., :3]
          :class:`numpy.ndarray`
              A batch of faces ready for feeding into the Faceswap model.
          """
          logger.trace("Compiling feed face. Batchsize: %s", len(feed_faces))  # type:ignore
        retval = np.stack([T.cast(np.ndarray, feed_face.face)[..., :3]
                             for feed_face in feed_faces]) / 255.0
          logger.trace("Compiled Feed faces. Shape: %s", retval.shape)  # type:ignore
          return retval
  
      def _predict(self, feed_faces: np.ndarray, batch_size: Optional[int] = None) -> np.ndarray:
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