          logger.debug("Compiled Samples: %s", {k: [i.shape for i in v] for k, v in retval.items()})
          return retval
  
      def set_timelapse_feed(self,
                             images: Dict[Literal["a", "b"], List[str]],
                             batch_size: int) -> None:
          """ Set the time-lapse feed for this feeder.
  
          Creates a generator from :class:`lib.training_data.PreviewDataGenerator` specifically
          for generating time-lapse previews for the feeder.
 
</s> remove     def _set_preview_feed(self) -> Dict[Literal["a", "b"], Generator[BatchType, None, None]]:
 </s> add     def _set_preview_feed(self) -> T.Dict[Literal["a", "b"], T.Generator[BatchType, None, None]]: </s> remove                  images: Dict[Literal["a", "b"], List[str]],
                 model: 'ModelBase',
 </s> add                  images: T.Dict[Literal["a", "b"], T.List[str]],
                 model: ModelBase, </s> remove                  config: Dict[str, "ConfigValueType"]) -> None:
 </s> add                  config: T.Dict[str, ConfigValueType]) -> None: </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                  model: "ModelBase",
                 images: Dict[Literal["a", "b"], List[str]],
 </s> add                  model: ModelBase,
                 images: T.Dict[Literal["a", "b"], T.List[str]],
                           images: T.Dict[Literal["a", "b"], T.List[str]],
          logger.debug("Compiled Samples: %s", {k: [i.shape for i in v] for k, v in retval.items()})
          return retval
  
      def set_timelapse_feed(self,
                           images: T.Dict[Literal["a", "b"], T.List[str]],
                             batch_size: int) -> None:
          """ Set the time-lapse feed for this feeder.
  
          Creates a generator from :class:`lib.training_data.PreviewDataGenerator` specifically
          for generating time-lapse 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