          self._output_file = output
          logger.debug("Time-lapse output set to '%s'", self._output_file)
  
          # Rewrite paths to pull from the training images so mask and face data can be accessed
          images: Dict[Literal["a", "b"], List[str]] = {}
          for side, input_ in zip(get_args(Literal["a", "b"]), (input_a, input_b)):
              training_path = os.path.dirname(self._image_paths[side][0])
              images[side] = [os.path.join(training_path, os.path.basename(pth))
                              for pth in get_image_paths(input_)]
  
 
</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"])
 </s> add         iterator = T.cast(T.Dict[Literal["a", "b"], T.Generator[BatchType, None, None]],
                          self._display_feeds["timelapse"]) </s> remove         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:]]

 </s> add  </s> remove         retval: Dict[Literal["a", "b"], List[np.ndarray]] = {}
 </s> add         retval: T.Dict[Literal["a", "b"], T.List[np.ndarray]] = {}
        images: T.Dict[Literal["a", "b"], T.List[str]] = {}
          self._output_file = output
          logger.debug("Time-lapse output set to '%s'", self._output_file)
  
          # Rewrite paths to pull from the training images so mask and face data can be accessed
        images: T.Dict[Literal["a", "b"], T.List[str]] = {}
          for side, input_ in zip(get_args(Literal["a", "b"]), (input_a, input_b)):
              training_path = os.path.dirname(self._image_paths[side][0])
              images[side] = [os.path.join(training_path, os.path.basename(pth))
                              for pth in get_image_paths(input_)]
  
keep keep keep keep 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