          assert isinstance(batch, MaskerBatch)
          mean = (0.384, 0.314, 0.279) if self._is_faceswap else (0.485, 0.456, 0.406)
          std = (0.324, 0.286, 0.275) if self._is_faceswap else (0.229, 0.224, 0.225)
  
          batch.feed = ((np.array([cast(np.ndarray, feed.face)[..., :3]
                                   for feed in batch.feed_faces],
                                  dtype="float32") / 255.0) - mean) / std
          logger.trace("feed shape: %s", batch.feed.shape)  # type:ignore
  
      def predict(self, feed: np.ndarray) -> np.ndarray:
 
</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         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         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         predicted: List[np.ndarray] = inbound if isinstance(inbound, list) else [inbound]
 </s> add         predicted: T.List[np.ndarray] = inbound if isinstance(inbound, list) else [inbound] </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:
        batch.feed = ((np.array([T.cast(np.ndarray, feed.face)[..., :3]
          assert isinstance(batch, MaskerBatch)
          mean = (0.384, 0.314, 0.279) if self._is_faceswap else (0.485, 0.456, 0.406)
          std = (0.324, 0.286, 0.275) if self._is_faceswap else (0.229, 0.224, 0.225)
  
        batch.feed = ((np.array([T.cast(np.ndarray, feed.face)[..., :3]
                                   for feed in batch.feed_faces],
                                  dtype="float32") / 255.0) - mean) / std
          logger.trace("feed shape: %s", batch.feed.shape)  # type:ignore
  
      def predict(self, feed: np.ndarray) -> 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 
plugins/extract/mask/bisenet_fp.py