      def _selected_model(self):
          """ dict: The selected encoder model options dictionary """
          arch = self._config["enc_architecture"]
          model = _MODEL_MAPPING.get(arch)
          model["kwargs"] = self._model_kwargs.get(arch, dict())
          return model
  
      @property
      def _model_input_shape(self):
          """ tuple: The required input shape for the encoder model.
 
</s> remove         return KerasModel(input_, outputs=outputs, name="decoder_{}".format(side))
 </s> add         return KerasModel(input_, outputs=outputs, name=f"decoder_{side}")
 </s> remove             var_y = Conv2DOutput(1, 5, name="mask_out_{}".format(side))(var_y)
 </s> add             var_y = Conv2DOutput(1, 5, name=f"mask_out_{side}")(var_y)
 </s> remove         return KerasModel(input_, outputs=outputs, name="decoder_{}".format(side))
 </s> add         return KerasModel(input_, outputs=outputs, name=f"decoder_{side}") </s> remove         encoder = getattr(self, "encoder_{}".format(self.architecture))()
 </s> add         encoder = getattr(self, f"encoder_{self.architecture}")() </s> remove         ret = list()
 </s> add         ret = []
        model["kwargs"] = self._model_kwargs.get(arch, {})
      def _selected_model(self):
          """ dict: The selected encoder model options dictionary """
          arch = self._config["enc_architecture"]
          model = _MODEL_MAPPING.get(arch)
        model["kwargs"] = self._model_kwargs.get(arch, {})
          return model
  
      @property
      def _model_input_shape(self):
          """ tuple: The required input shape for the encoder model.
keep keep keep keep replace keep keep keep keep keep
 Update all Keras Imports to be conditional (#1214)

* Remove custom keras importer

* first round keras imports fix

* launcher.py: Remove KerasFinder references

* 2nd round keras imports update (lib and extract)

* 3rd round keras imports update (train)

* remove KerasFinder from tests

* 4th round keras imports update (tests) 
plugins/train/model/phaze_a.py