                             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:
          """ Run the Faceswap models' prediction function.
  
          Parameters
          ----------
          feed_faces: :class:`numpy.ndarray`
 
</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] </s> remove     def _get_predictions(self, feed_a: np.ndarray, feed_b: np.ndarray) -> Dict[str, np.ndarray]:
 </s> add     def _get_predictions(self, feed_a: np.ndarray, feed_b: np.ndarray) -> T.Dict[str, 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                         batch_size: Optional[int] = None,
                        images: Optional[List[str]] = None) -> DataGenerator:
 </s> add                         batch_size: T.Optional[int] = None,
                        images: T.Optional[T.List[str]] = None) -> DataGenerator: </s> remove         this is a straight call to the predict function. For PlaidML backends, this attempts
        to optimize the inference batch sizes to reduce the number of kernels that need to be
        compiled.
 </s> add         this is a straight call to the predict function.
    def _predict(self, feed_faces: np.ndarray, batch_size: T.Optional[int] = None) -> np.ndarray:
                             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: T.Optional[int] = None) -> np.ndarray:
          """ Run the Faceswap models' prediction function.
  
          Parameters
          ----------
          feed_faces: :class:`numpy.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