                                                  True,
                                                  batch_size=batchsize).minibatch_ab()
          return retval
  
      def get_batch(self) -> Tuple[List[List[np.ndarray]], ...]:
          """ Get the feed data and the targets for each training side for feeding into the model's
          train function.
  
          Returns
          -------
 
</s> remove     def _get_mask_channels(self) -> List[int]:
 </s> add     def _get_mask_channels(self) -> T.List[int]: </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         model_inputs: List[List[np.ndarray]] = []
        model_targets: List[List[np.ndarray]] = []
 </s> add         model_inputs: T.List[T.List[np.ndarray]] = []
        model_targets: T.List[T.List[np.ndarray]] = [] </s> remove     def _get_config(self, configfile: Optional[str]) -> Dict[str, "ConfigValueType"]:
 </s> add     def _get_config(self, configfile: T.Optional[str]) -> T.Dict[str, ConfigValueType]: </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 get_batch(self) -> T.Tuple[T.List[T.List[np.ndarray]], ...]:
                                                  True,
                                                  batch_size=batchsize).minibatch_ab()
          return retval
  
    def get_batch(self) -> T.Tuple[T.List[T.List[np.ndarray]], ...]:
          """ Get the feed data and the targets for each training side for feeding into the model's
          train function.
  
          Returns
          -------
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/train/trainer/_base.py