  
          self._tensorboard = self._set_tensorboard()
          self._samples = _Samples(self._model,
                                   self._model.coverage_ratio,
                                   cast(int, self._config["mask_opacity"]),
                                   cast(str, self._config["mask_color"]))
  
          num_images = self._config.get("preview_images", 14)
          assert isinstance(num_images, int)
          self._timelapse = _Timelapse(self._model,
                                       self._model.coverage_ratio,
 
</s> remove                                      cast(int, self._config["mask_opacity"]),
                                     cast(str, self._config["mask_color"]),
 </s> add                                      T.cast(int, self._config["mask_opacity"]),
                                     T.cast(str, self._config["mask_color"]), </s> remove         retval: Dict[Literal["a", "b"], List[np.ndarray]] = {}
 </s> add         retval: T.Dict[Literal["a", "b"], T.List[np.ndarray]] = {} </s> remove         retval: Dict[Literal["a", "b"], Generator[BatchType, None, None]] = {}
 </s> add         retval: T.Dict[Literal["a", "b"], T.Generator[BatchType, None, None]] = {} </s> remove     def _get_slices(self, dimensions: int) -> List[Tuple[int, ...]]:
 </s> add     def _get_slices(self, dimensions: int) -> T.List[T.Tuple[int, ...]]: </s> remove     file = ('{"test": "param"}')
 </s> add     file = '{"test": "param"}'
                                 T.cast(int, self._config["mask_opacity"]),
                                 T.cast(str, self._config["mask_color"]))
  
          self._tensorboard = self._set_tensorboard()
          self._samples = _Samples(self._model,
                                   self._model.coverage_ratio,
                                 T.cast(int, self._config["mask_opacity"]),
                                 T.cast(str, self._config["mask_color"]))
                                 T.cast(int, self._config["mask_opacity"]),
                                 T.cast(str, self._config["mask_color"]))
  
          num_images = self._config.get("preview_images", 14)
          assert isinstance(num_images, int)
          self._timelapse = _Timelapse(self._model,
                                       self._model.coverage_ratio,
keep keep keep keep 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