          infs = sum(np.count_nonzero(np.isinf(w)) for w in weights)
  
          if nans + infs == 0:
              return {}
          return dict(nans=nans, infs=infs)
  
      def _parse_output(self, errors: dict, indent: int = 0) -> None:
          """ Parse the output of the errors dictionary and print a pretty summary.
  
          Parameters
 
</s> remove     def _get_nodes(self, nodes: np.ndarray) -> List[Tuple[str, int]]:
 </s> add     def _get_nodes(self, nodes: np.ndarray) -> T.List[T.Tuple[str, int]]: </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] = {} </s> remove     no_amd: bool = False
    tf_min: Tuple[int, int] = (2, 0)
    scaling: Tuple[int, int] = (0, 1)
 </s> add     tf_min: T.Tuple[int, int] = (2, 0)
    scaling: T.Tuple[int, int] = (0, 1) </s> remove     def _rewrite_plaid_outputs(self) -> None:
        """ Rewrite the output names for models using the PlaidML (Keras 2.2.4) backend

        Keras 2.2.4 duplicates model output names if any of the models have multiple outputs
        so we need to rename the outputs so we can successfully map the loss dictionaries.

        This is a bit of a hack, but it does work.
        """
        # TODO Remove this rewrite code if PlaidML updates to a version of Keras where this is
        # no longer necessary
        if len(self.model.output_names) == len(set(self.model.output_names)):
            logger.debug("Output names are unique, not rewriting: %s", self.model.output_names)
            return
        seen = {name: 0 for name in set(self.model.output_names)}
        new_names = []
        for name in self.model.output_names:
            new_names.append(f"{name}_{seen[name]}")
            seen[name] += 1
        logger.debug("Output names rewritten: (old: %s, new: %s)",
                     self.model.output_names, new_names)
        self.model.output_names = new_names

    def _legacy_mapping(self) -> Optional[dict]:
 </s> add     def _legacy_mapping(self) -> T.Optional[dict]: </s> remove         preds: Dict[str, np.ndarray] = {}
 </s> add         preds: T.Dict[str, np.ndarray] = {}
        return {"nans": nans, "infs": infs}
          infs = sum(np.count_nonzero(np.isinf(w)) for w in weights)
  
          if nans + infs == 0:
              return {}
        return {"nans": nans, "infs": infs}
  
      def _parse_output(self, errors: dict, indent: int = 0) -> None:
          """ Parse the output of the errors dictionary and print a pretty summary.
  
          Parameters
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 
tools/model/model.py