          var_x3 = LeakyReLU(alpha=0.2)(var_x3)
          var_x3 = ResidualBlock(dims * 2)(var_x3)
          var_x3 = ResidualBlock(dims * 2)(var_x3)
  
          outputs.append(Conv2DOutput(3, 5, name="face_out_128_{}".format(side))(var_x3))
  
          if self.use_mask:
              var_y = input_
              var_y = UpscaleBlock(self.decoder_dim * 8, activation="leakyrelu")(var_y)
              var_y = UpscaleBlock(self.decoder_dim * 4, activation="leakyrelu")(var_y)
 
</s> remove             outputs.append(Conv2DOutput(3, 5, name="face_out_64_{}".format(side))(var_x2))
 </s> add             outputs.append(Conv2DOutput(3, 5, name=f"face_out_64_{side}")(var_x2)) </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             outputs.append(Conv2DOutput(3, 5, name="face_out_32_{}".format(side))(var_x1))
 </s> add             outputs.append(Conv2DOutput(3, 5, name=f"face_out_32_{side}")(var_x1)) </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)
        outputs.append(Conv2DOutput(3, 5, name=f"face_out_128_{side}")(var_x3))
          var_x3 = LeakyReLU(alpha=0.2)(var_x3)
          var_x3 = ResidualBlock(dims * 2)(var_x3)
          var_x3 = ResidualBlock(dims * 2)(var_x3)
  
        outputs.append(Conv2DOutput(3, 5, name=f"face_out_128_{side}")(var_x3))
  
          if self.use_mask:
              var_y = input_
              var_y = UpscaleBlock(self.decoder_dim * 8, activation="leakyrelu")(var_y)
              var_y = UpscaleBlock(self.decoder_dim * 4, activation="leakyrelu")(var_y)
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/dfl_sae.py