          """ Toggle the mask overlay on or off based on user input. """
          self._samples.toggle_mask_display()
  
      def train_one_step(self,
                         viewer: Optional[Callable[[np.ndarray, str], None]],
                         timelapse_kwargs: Optional[Dict[Literal["input_a",
                                                                 "input_b",
                                                                 "output"], str]]) -> None:
          """ Running training on a batch of images for each side.
  
          Triggered from the training cycle in :class:`scripts.train.Train`.
  
          * Runs a training batch through the model.
 
</s> remove                         viewer: Optional[Callable[[np.ndarray, str], None]],
                        timelapse_kwargs: Optional[Dict[Literal["input_a",
                                                                "input_b",
                                                                "output"], str]]) -> None:
 </s> add                         viewer: T.Optional[T.Callable[[np.ndarray, str], None]],
                        timelapse_kwargs: T.Optional[T.Dict[Literal["input_a",
                                                                    "input_b",
                                                                    "output"], str]]) -> None: </s> remove                  model: "ModelBase",
 </s> add                  model: ModelBase, </s> remove     def output_timelapse(self, timelapse_kwargs: Dict[Literal["input_a",
                                                              "input_b",
                                                              "output"], str]) -> None:
 </s> add     def output_timelapse(self, timelapse_kwargs: T.Dict[Literal["input_a",
                                                                "input_b",
                                                                "output"], str]) -> None: </s> remove                  model: "ModelBase",
 </s> add                  model: ModelBase, </s> remove     def _process_batch(self, batch: List[ConvertItem], faces_seen: int):
 </s> add     def _process_batch(self, batch: T.List[ConvertItem], faces_seen: int):
                       viewer: T.Optional[T.Callable[[np.ndarray, str], None]],
                       timelapse_kwargs: T.Optional[T.Dict[Literal["input_a",
                                                                   "input_b",
                                                                   "output"], str]]) -> None:
          """ Toggle the mask overlay on or off based on user input. """
          self._samples.toggle_mask_display()
  
      def train_one_step(self,
                       viewer: T.Optional[T.Callable[[np.ndarray, str], None]],
                       timelapse_kwargs: T.Optional[T.Dict[Literal["input_a",
                                                                   "input_b",
                                                                   "output"], str]]) -> None:
                       viewer: T.Optional[T.Callable[[np.ndarray, str], None]],
                       timelapse_kwargs: T.Optional[T.Dict[Literal["input_a",
                                                                   "input_b",
                                                                   "output"], str]]) -> None:
                       viewer: T.Optional[T.Callable[[np.ndarray, str], None]],
                       timelapse_kwargs: T.Optional[T.Dict[Literal["input_a",
                                                                   "input_b",
                                                                   "output"], str]]) -> None:
                       viewer: T.Optional[T.Callable[[np.ndarray, str], None]],
                       timelapse_kwargs: T.Optional[T.Dict[Literal["input_a",
                                                                   "input_b",
                                                                   "output"], str]]) -> None:
          """ Running training on a batch of images for each side.
  
          Triggered from the training cycle in :class:`scripts.train.Train`.
  
          * Runs a training batch through the model.
keep keep keep keep replace 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/train/trainer/_base.py