              samples, targets and masks for this preview
          """
          logger.debug("Generating preview (is_timelapse: %s)", is_timelapse)
  
          batchsizes: List[int] = []
          feed: Dict[Literal["a", "b"], np.ndarray] = {}
          samples: Dict[Literal["a", "b"], np.ndarray] = {}
          masks: Dict[Literal["a", "b"], np.ndarray] = {}
  
          # MyPy can't recurse into nested dicts to get the type :(
          iterator = cast(Dict[Literal["a", "b"], Generator[BatchType, None, None]],
                          self._display_feeds["timelapse" if is_timelapse else "preview"])
          for side in get_args(Literal["a", "b"]):
 
</s> remove         iterator = cast(Dict[Literal["a", "b"], Generator[BatchType, None, None]],
                        self._display_feeds["timelapse" if is_timelapse else "preview"])
 </s> add         iterator = T.cast(T.Dict[Literal["a", "b"], T.Generator[BatchType, None, None]],
                          self._display_feeds["timelapse" if is_timelapse else "preview"]) </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         figures: Dict[Literal["a", "b"], np.ndarray] = {}
        headers: Dict[Literal["a", "b"], np.ndarray] = {}
 </s> add         figures: T.Dict[Literal["a", "b"], np.ndarray] = {}
        headers: T.Dict[Literal["a", "b"], np.ndarray] = {} </s> remove                        feed: Dict[Literal["a", "b"], np.ndarray],
                       samples: Dict[Literal["a", "b"], np.ndarray],
                       masks: Dict[Literal["a", "b"], np.ndarray]
                       ) -> Dict[Literal["a", "b"], List[np.ndarray]]:
 </s> add                        feed: T.Dict[Literal["a", "b"], np.ndarray],
                       samples: T.Dict[Literal["a", "b"], np.ndarray],
                       masks: T.Dict[Literal["a", "b"], np.ndarray]
                       ) -> 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]] = {}
        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] = {}
              samples, targets and masks for this preview
          """
          logger.debug("Generating preview (is_timelapse: %s)", is_timelapse)
  
        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] = {}
        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] = {}
        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] = {}
        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] = {}
  
          # MyPy can't recurse into nested dicts to get the type :(
          iterator = cast(Dict[Literal["a", "b"], Generator[BatchType, None, None]],
                          self._display_feeds["timelapse" if is_timelapse else "preview"])
          for side in get_args(Literal["a", "b"]):
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