              del model
              model = new_model
          return model
  
      def _select_freeze_layers(self) -> List[str]:
          """ Process the selected frozen layers and replace the `keras_encoder` option with the
          actual keras model name
  
          Returns
          -------
 
</s> remove     def strategy_scope(self) -> ContextManager:
 </s> add     def strategy_scope(self) -> T.ContextManager: </s> remove         if get_backend() == "amd":
            logger.debug("Mixed Precision not supported for AMD. Returning empty list")
            return []
 </s> add  </s> remove         return KerasModel(input_, var_x, name="encoder")
 </s> add         return KModel(input_, var_x, name="encoder") </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     def _get_mixed_precision_layers(self, layers: List[dict]) -> List[str]:
 </s> add     def _get_mixed_precision_layers(self, layers: T.List[dict]) -> T.List[str]:
    def _select_freeze_layers(self) -> T.List[str]:
              del model
              model = new_model
          return model
  
    def _select_freeze_layers(self) -> T.List[str]:
          """ Process the selected frozen layers and replace the `keras_encoder` option with the
          actual keras model name
  
          Returns
          -------
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/model/phaze_a.py