                             self._images[side] if images is None else images,
                             self._batch_size if batch_size is None else batch_size)
          return retval
  
      def _set_preview_feed(self) -> Dict[Literal["a", "b"], Generator[BatchType, None, None]]:
          """ Set the preview feed for this feeder.
  
          Creates a generator from :class:`lib.training_data.PreviewDataGenerator` specifically
          for previews for the feeder.
  
 
</s> remove                            images: Dict[Literal["a", "b"], List[str]],
 </s> add                            images: T.Dict[Literal["a", "b"], T.List[str]], </s> remove         retval: Optional[Callable[[np.ndarray],
                                  List[bytes]]] = None if test is None else self._writer.pre_encode
 </s> add         retval: T.Optional[T.Callable[[np.ndarray],
                           T.List[bytes]]] = None if test is None else self._writer.pre_encode </s> remove         batchsizes: List[int] = []
        feed: Dict[Literal["a", "b"], np.ndarray] = {}
        samples: Dict[Literal["a", "b"], np.ndarray] = {}
        masks: Dict[Literal["a", "b"], np.ndarray] = {}
 </s> add         batchsizes: T.List[int] = []
        feed: T.Dict[Literal["a", "b"], np.ndarray] = {}
        samples: T.Dict[Literal["a", "b"], np.ndarray] = {}
        masks: T.Dict[Literal["a", "b"], np.ndarray] = {} </s> remove         iterator = cast(Dict[Literal["a", "b"], Generator[BatchType, None, None]],
                        self._display_feeds["timelapse"])
 </s> add         iterator = T.cast(T.Dict[Literal["a", "b"], T.Generator[BatchType, None, None]],
                          self._display_feeds["timelapse"]) </s> remove     def _mask_shapes(self) -> Optional[List[tuple]]:
 </s> add     def _mask_shapes(self) -> T.Optional[T.List[tuple]]:
    def _set_preview_feed(self) -> T.Dict[Literal["a", "b"], T.Generator[BatchType, None, None]]:
                             self._images[side] if images is None else images,
                             self._batch_size if batch_size is None else batch_size)
          return retval
  
    def _set_preview_feed(self) -> T.Dict[Literal["a", "b"], T.Generator[BatchType, None, None]]:
          """ Set the preview feed for this feeder.
  
          Creates a generator from :class:`lib.training_data.PreviewDataGenerator` specifically
          for previews for the feeder.
  
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