          otherwise ``False`` """
          return self._writer.config.get("draw_transparent", False)
  
      @property
      def pre_encode(self) -> Optional[Callable[[np.ndarray], List[bytes]]]:
          """ python function: Selected writer's pre-encode function, if it has one,
          otherwise ``None`` """
          dummy = np.zeros((20, 20, 3), dtype="uint8")
          test = self._writer.pre_encode(dummy)
          retval: Optional[Callable[[np.ndarray],
 
</s> remove         retval: Optional[Callable[[np.ndarray],
                                  List[bytes]]] = None if test is None else self._writer.pre_encode
 </s> add         retval: T.Optional[T.Callable[[np.ndarray],
                           T.List[bytes]]] = None if test is None else self._writer.pre_encode </s> remove         if get_backend() == "amd":
            logger.debug("No action to perform for 'mixed_precision' on backend '%s': "
                         "use_mixed_precision: %s)", get_backend(), use_mixed_precision)
            return False

 </s> add  </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                       ) -> Optional[tf.distribute.Strategy]:
 </s> add                       ) -> T.Optional[tf.distribute.Strategy]: </s> remove     _backends = (("nvidia", "amd", "apple_silicon", "directml", "rocm", "cpu"))
 </s> add     _backends = (("nvidia", "apple_silicon", "directml", "rocm", "cpu"))
    def pre_encode(self) -> T.Optional[T.Callable[[np.ndarray], T.List[bytes]]]:
          otherwise ``False`` """
          return self._writer.config.get("draw_transparent", False)
  
      @property
    def pre_encode(self) -> T.Optional[T.Callable[[np.ndarray], T.List[bytes]]]:
          """ python function: Selected writer's pre-encode function, if it has one,
          otherwise ``None`` """
          dummy = np.zeros((20, 20, 3), dtype="uint8")
          test = self._writer.pre_encode(dummy)
          retval: Optional[Callable[[np.ndarray],
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 
scripts/convert.py