                              (48, 48),
                              dst=feed_batch[idx])
                   for idx, rect in enumerate(rectangles)]
  
              cls_probs, roi_probs, pts_probs = self.predict(
                  feed_batch,
                  batch_size=128 if get_backend() == "amd" else None)
              ret.append(self._filter_face_48net(cls_probs, roi_probs, pts_probs, rectangles))
          return ret
  
      def _filter_face_48net(self, class_probabilities: np.ndarray,
                             roi: np.ndarray,
 
</s> remove                            rectangles: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
 </s> add                            rectangles: np.ndarray) -> T.Tuple[np.ndarray, np.ndarray]: </s> remove                            scale: float) -> Tuple[np.ndarray, np.ndarray]:
 </s> add                            scale: float) -> T.Tuple[np.ndarray, 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         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         method: str = "iom") -> Tuple[np.ndarray, np.ndarray]:
 </s> add         method: str = "iom") -> T.Tuple[np.ndarray, np.ndarray]:
            cls_probs, roi_probs, pts_probs = self.predict(feed_batch)
                              (48, 48),
                              dst=feed_batch[idx])
                   for idx, rect in enumerate(rectangles)]
  
            cls_probs, roi_probs, pts_probs = self.predict(feed_batch)
            cls_probs, roi_probs, pts_probs = self.predict(feed_batch)
            cls_probs, roi_probs, pts_probs = self.predict(feed_batch)
              ret.append(self._filter_face_48net(cls_probs, roi_probs, pts_probs, rectangles))
          return ret
  
      def _filter_face_48net(self, class_probabilities: np.ndarray,
                             roi: np.ndarray,
keep keep keep keep replace replace 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/detect/mtcnn.py