          PlaidML has a bug regarding the clip-norm parameter See:
          https://github.com/plaidml/plaidml/issues/228. We workaround by simply not adding this
          parameter for AMD backend users.
          """
          lr_key = "lr" if get_backend() == "amd" else "learning_rate"
          self._kwargs[lr_key] = learning_rate
  
          if not autoclip:
              return
  
          logger.info("Enabling AutoClip")
          self._kwargs["gradient_transformers"] = [AutoClipper(10, history_size=10000)]
 
</s> remove 
        Notes
        -----
        Clip-norm is ballooning VRAM usage, which is not expected behavior and may be a bug in
        Keras/Tensorflow.

        PlaidML has a bug regarding the clip-norm parameter See:
        https://github.com/plaidml/plaidml/issues/228. We workaround by simply not adding this
        parameter for AMD backend users.
 </s> add  </s> remove     else:
        signature = inspect.signature(func)
        parameter = signature.parameters.get(name)
        if parameter is None:
            if accept_all:
                for param in signature.parameters.values():
                    if param.kind == inspect.Parameter.VAR_KEYWORD:
                        return True
            return False
        return (parameter.kind in (inspect.Parameter.POSITIONAL_OR_KEYWORD,
                                   inspect.Parameter.KEYWORD_ONLY))
 </s> add     signature = inspect.signature(func)
    parameter = signature.parameters.get(name)
    if parameter is None:
        if accept_all:
            for param in signature.parameters.values():
                if param.kind == inspect.Parameter.VAR_KEYWORD:
                    return True
        return False
    return (parameter.kind in (inspect.Parameter.POSITIONAL_OR_KEYWORD,
                               inspect.Parameter.KEYWORD_ONLY)) </s> remove         self._mixed_precision = self.config["mixed_precision"] and get_backend() != "amd"
 </s> add         self._mixed_precision = self.config["mixed_precision"] </s> remove         if get_backend() == "amd" and model.no_amd:
            valid = [k for k, v in _MODEL_MAPPING.items() if not v.no_amd]
            raise FaceswapError(f"'{arch}' is not compatible with the AMD backend. Choose one of "
                                f"{valid}.")

 </s> add  </s> remove             logger.verbose("Hiding GPUs from Tensorflow")  # type:ignore
 </s> add             logger.verbose("Hiding GPUs from Tensorflow")  # type:ignore[attr-defined]
        self._kwargs["learning_rate"] = learning_rate
          PlaidML has a bug regarding the clip-norm parameter See:
          https://github.com/plaidml/plaidml/issues/228. We workaround by simply not adding this
          parameter for AMD backend users.
          """
        self._kwargs["learning_rate"] = learning_rate
        self._kwargs["learning_rate"] = learning_rate
        self._kwargs["learning_rate"] = learning_rate
          if not autoclip:
              return
  
          logger.info("Enabling AutoClip")
          self._kwargs["gradient_transformers"] = [AutoClipper(10, history_size=10000)]
keep keep keep keep replace replace 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