              var_y = UpscaleBlock(128, activation="leakyrelu")(var_y)
              var_y = UpscaleBlock(64, activation="leakyrelu")(var_y)
              var_y = Conv2DOutput(1, 5, name="mask_out_{}".format(side))(var_y)
              outputs.append(var_y)
          return KerasModel([input_], outputs=outputs, name="decoder_{}".format(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                                  name="mask_out_{}".format(side))(var_y)
 </s> add                                  name=f"mask_out_{side}")(var_y)
 </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             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}")
        return KerasModel([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="mask_out_{}".format(side))(var_y)
              outputs.append(var_y)
        return KerasModel([input_], outputs=outputs, name=f"decoder_{side}")
keep keep keep keep replace
 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/dfaker.py