              var_y = UpscaleBlock(128, activation="leakyrelu")(var_y)
              var_y = UpscaleBlock(64, activation="leakyrelu")(var_y)
              var_y = Conv2DOutput(1, 5, name=f"mask_out_{side}")(var_y)
              outputs.append(var_y)
          return KerasModel(input_, outputs=outputs, name=f"decoder_{side}")
  
      def _legacy_mapping(self):
          """ The mapping of legacy separate model names to single model names """
          return {f"{self.name}_encoder.h5": "encoder",
                  f"{self.name}_decoder_A.h5": "decoder_a",
 
</s> remove         return KerasModel(input_, outputs=outputs, name="decoder")
 </s> add         return KModel(input_, outputs=outputs, name="decoder") </s> remove         return KerasModel(input_, outputs=outputs, name=f"decoder_{side}")
 </s> add         return KModel(input_, outputs=outputs, name=f"decoder_{side}") </s> remove         return KerasModel(input_, outputs=outputs, name="decoder_b")
 </s> add         return KModel(input_, outputs=outputs, name="decoder_b") </s> remove         return KerasModel(input_, outputs=outputs, name="decoder_a")
 </s> add         return KModel(input_, outputs=outputs, name="decoder_a") </s> remove         return KerasModel([input_], outputs=outputs, name="decoder_b")
 </s> add         return KModel([input_], outputs=outputs, name="decoder_b")
        return KModel(input_, outputs=outputs, name=f"decoder_{side}")

              var_y = UpscaleBlock(128, activation="leakyrelu")(var_y)
              var_y = UpscaleBlock(64, activation="leakyrelu")(var_y)
              var_y = Conv2DOutput(1, 5, name=f"mask_out_{side}")(var_y)
              outputs.append(var_y)
        return KModel(input_, outputs=outputs, name=f"decoder_{side}")
  
      def _legacy_mapping(self):
          """ The mapping of legacy separate model names to single model names """
          return {f"{self.name}_encoder.h5": "encoder",
                  f"{self.name}_decoder_A.h5": "decoder_a",
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/original.py