                      not self.use_mixed_precision, self.use_mixed_precision)
          del model
          return new_model
  
      def strategy_scope(self) -> ContextManager:
          """ Return the strategy scope if we have set a strategy, otherwise return a null
          context.
  
          Returns
          -------
 
</s> remove                       ) -> Optional[tf.distribute.Strategy]:
 </s> add                       ) -> T.Optional[tf.distribute.Strategy]: </s> remove     def _select_freeze_layers(self) -> List[str]:
 </s> add     def _select_freeze_layers(self) -> T.List[str]: </s> remove         if get_backend() == "amd":  # Mixed precision not supported on amd
            return model

 </s> add  </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 strategy_scope(self) -> T.ContextManager:
                      not self.use_mixed_precision, self.use_mixed_precision)
          del model
          return new_model
  
    def strategy_scope(self) -> T.ContextManager:
          """ Return the strategy scope if we have set a strategy, otherwise return a null
          context.
  
          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/_base/settings.py