          list:
              List of :class:`numpy.ndarray` of predictions received from the model
          """
          logger.debug("Getting Predictions")
          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
 
</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         masks3: Union[List[np.ndarray], np.ndarray] = []
 </s> add         masks3: T.Union[T.List[np.ndarray], 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         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 _CONFIG: Dict[str, "ConfigValueType"] = {}


def KerasModel(inputs: list, outputs: list, name: str) -> keras.models.Model:  # noqa, pylint:disable=invalid-name
    """ wrapper for :class:`keras.models.Model`.

    There are some minor foibles between Keras 2.2 and the Tensorflow version of Keras, so this
    catches potential issues and fixes prior to returning the requested model.

    All models created within plugins should use this method, and should not call keras directly
    for a model.

    Parameters
    ----------
    inputs: a keras.Input object or list of keras.Input objects.
        The input(s) of the model
    outputs: keras objects
        The output(s) of the model.
    name: str
        The name of the model.

    Returns
    -------
    :class:`keras.models.Model`
        A Keras Model
    """
    if get_backend() == "amd":
        logger.debug("Flattening inputs (%s) and outputs (%s) for AMD", inputs, outputs)
        inputs = np.array(inputs).flatten().tolist()
        outputs = np.array(outputs).flatten().tolist()
        logger.debug("Flattened inputs (%s) and outputs (%s)", inputs, outputs)
    return KModel(inputs, outputs, name=name)
 </s> add _CONFIG: T.Dict[str, ConfigValueType] = {}
        preds: T.Dict[str, np.ndarray] = {}
          list:
              List of :class:`numpy.ndarray` of predictions received from the model
          """
          logger.debug("Getting Predictions")
        preds: T.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
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