          ----------
          inputs: tensor
              Input tensor, or list/tuple of input tensors
          """
          if get_backend() == "amd":
              return inputs * K.sigmoid(inputs * self.beta)
          # Native TF Implementation has more memory-efficient gradients
          return tf.nn.swish(inputs * self.beta)
  
      def get_config(self):
          """Returns the config of the layer.
  
 
</s> remove     def __call__(self, inputs: Union[Tensor, List[Tensor]]) -> Union[Tensor, List[Tensor]]:
 </s> add     def __call__(self, inputs: T.Union[Tensor, T.List[Tensor]]) -> T.Union[Tensor, T.List[Tensor]]: </s> remove             if get_backend() != "amd":
                retval = tf.image.resize(inputs, (size, size), method=self.interpolation)
            else:
                raise NotImplementedError
 </s> add             retval = tf.image.resize(inputs, (size, size), method=self.interpolation) </s> remove         return pad(var_x,
                   [[0, 0],
                    [padding_top, padding_bot],
                    [padding_left, padding_right],
                    [0, 0]],
                   'REFLECT')
 </s> add         return tf.pad(inputs,
                      [[0, 0],
                       [padding_top, padding_bot],
                       [padding_left, padding_right],
                       [0, 0]],
                      'REFLECT') </s> remove     def compute_output_shape(self, input_shape: Tuple[int, ...]) -> Tuple[int, ...]:
 </s> add     def compute_output_shape(self, input_shape: T.Tuple[int, ...]) -> T.Tuple[int, ...]: </s> remove                        samples: List[np.ndarray],
                       predictions: List[np.ndarray]) -> List[np.ndarray]:
 </s> add                        samples: T.List[np.ndarray],
                       predictions: T.List[np.ndarray]) -> T.List[np.ndarray]:
          ----------
          inputs: tensor
              Input tensor, or list/tuple of input tensors
          """



          return tf.nn.swish(inputs * self.beta)
  
      def get_config(self):
          """Returns the config of the layer.
  
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 
lib/model/layers.py