          preds: Dict[str, np.ndarray] = {}
          standard = self._model.model.predict([feed_a, feed_b], verbose=0)
          swapped = self._model.model.predict([feed_b, feed_a], verbose=0)
  
          if self._model.config["learn_mask"] and get_backend() == "amd":
              # Ravel results for plaidml
              split = len(standard) // 2
              standard = [standard[:split], standard[split:]]
              swapped = [swapped[:split], swapped[split:]]
  
          if self._model.config["learn_mask"]:  # Add mask to 4th channel of final output
              standard = [np.concatenate(side[-2:], axis=-1) for side in standard]
              swapped = [np.concatenate(side[-2:], axis=-1) for side in swapped]
          else:  # Retrieve final output
              standard = [side[-1] if isinstance(side, list) else side for side in standard]
 
</s> remove         preds: Dict[str, np.ndarray] = {}
 </s> add         preds: T.Dict[str, np.ndarray] = {} </s> remove         model_inputs: List[List[np.ndarray]] = []
        model_targets: List[List[np.ndarray]] = []
 </s> add         model_inputs: T.List[T.List[np.ndarray]] = []
        model_targets: T.List[T.List[np.ndarray]] = [] </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         batchsizes: List[int] = []
        feed: Dict[Literal["a", "b"], np.ndarray] = {}
        samples: Dict[Literal["a", "b"], np.ndarray] = {}
        masks: Dict[Literal["a", "b"], np.ndarray] = {}
 </s> add         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] = {} </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"])
          preds: Dict[str, np.ndarray] = {}
          standard = self._model.model.predict([feed_a, feed_b], verbose=0)
          swapped = self._model.model.predict([feed_b, feed_a], verbose=0)
  






          if self._model.config["learn_mask"]:  # Add mask to 4th channel of final output
              standard = [np.concatenate(side[-2:], axis=-1) for side in standard]
              swapped = [np.concatenate(side[-2:], axis=-1) for side in swapped]
          else:  # Retrieve final output
              standard = [side[-1] if isinstance(side, list) else side for side in standard]
keep keep keep keep replace replace 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