                          self._num_images)
          self._feeder.set_timelapse_feed(images, batchsize)
          logger.debug("Set up time-lapse")
  
      def output_timelapse(self, timelapse_kwargs: Dict[Literal["input_a",
                                                                "input_b",
                                                                "output"], str]) -> None:
          """ Generate the time-lapse samples and output the created time-lapse to the specified
          output folder.
  
          Parameters
          ----------
 
</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                        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             self._setup(**cast(Dict[str, str], timelapse_kwargs))
 </s> add             self._setup(**T.cast(T.Dict[str, str], timelapse_kwargs)) </s> remove                            images: Dict[Literal["a", "b"], List[str]],
 </s> add                            images: T.Dict[Literal["a", "b"], T.List[str]], </s> remove     def finalize_predictions(self, bounding_boxes_scales: List[np.ndarray]) -> np.ndarray:
 </s> add     def finalize_predictions(self, bounding_boxes_scales: T.List[np.ndarray]) -> np.ndarray:
    def output_timelapse(self, timelapse_kwargs: T.Dict[Literal["input_a",
                                                                "input_b",
                                                                "output"], str]) -> None:
                          self._num_images)
          self._feeder.set_timelapse_feed(images, batchsize)
          logger.debug("Set up time-lapse")
  
    def output_timelapse(self, timelapse_kwargs: T.Dict[Literal["input_a",
                                                                "input_b",
                                                                "output"], str]) -> None:
    def output_timelapse(self, timelapse_kwargs: T.Dict[Literal["input_a",
                                                                "input_b",
                                                                "output"], str]) -> None:
    def output_timelapse(self, timelapse_kwargs: T.Dict[Literal["input_a",
                                                                "input_b",
                                                                "output"], str]) -> None:
          """ Generate the time-lapse samples and output the created time-lapse to the specified
          output folder.
  
          Parameters
          ----------
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/train/trainer/_base.py