              outputs = [decoder(inter_a), decoder(inter_b)]
          else:
              outputs = [self.decoder("a", enc_output_shape)(encoder_a),
                         self.decoder("b", enc_output_shape)(encoder_b)]
          autoencoder = KerasModel(inputs,
                                   outputs,
                                   name=self.model_name)
          return autoencoder
  
      def encoder_df(self):
          """ DFL SAE DF Encoder Network"""
          input_ = Input(shape=self.input_shape)
 
</s> remove         autoencoder = KerasModel(inputs, outputs, name=self.model_name)
 </s> add         autoencoder = KModel(inputs, outputs, name=self.model_name) </s> remove         autoencoder = KerasModel(inputs, outputs, name=self.model_name)
 </s> add         autoencoder = KModel(inputs, outputs, name=self.model_name) </s> remove         autoencoder = KerasModel(inputs, outputs, name=self.model_name)
 </s> add         autoencoder = KModel(inputs, outputs, name=self.model_name) </s> remove         autoencoder = KerasModel(inputs, outputs, name=self.model_name)
 </s> add         autoencoder = KModel(inputs, outputs, name=self.model_name) </s> remove         autoencoder = KerasModel(inputs, outputs, name=self.model_name)
 </s> add         autoencoder = KModel(inputs, outputs, name=self.model_name)

        autoencoder = KModel(inputs, outputs, name=self.model_name)
              outputs = [decoder(inter_a), decoder(inter_b)]
          else:
              outputs = [self.decoder("a", enc_output_shape)(encoder_a),
                         self.decoder("b", enc_output_shape)(encoder_b)]
        autoencoder = KModel(inputs, outputs, name=self.model_name)
        autoencoder = KModel(inputs, outputs, name=self.model_name)
        autoencoder = KModel(inputs, outputs, name=self.model_name)
          return autoencoder
  
      def encoder_df(self):
          """ DFL SAE DF Encoder Network"""
          input_ = Input(shape=self.input_shape)
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/dfl_sae.py