          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)(var_y)
              var_y = UpscaleBlock(self.decoder_dim * 4)(var_y)
              var_y = UpscaleBlock(self.decoder_dim * 2)(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))
  
      def _legacy_mapping(self):
 
</s> remove                 var_y = UpscaleBlock(decoder_complexity // 8)(var_y)
 </s> add                 var_y = UpscaleBlock(decoder_complexity // 8, activation="leakyrelu")(var_y) </s> remove             var_y = UpscaleBlock(256)(var_y)
            var_y = UpscaleBlock(128)(var_y)
            var_y = UpscaleBlock(64)(var_y)
 </s> add             var_y = UpscaleBlock(256, activation="leakyrelu")(var_y)
            var_y = UpscaleBlock(128, activation="leakyrelu")(var_y)
            var_y = UpscaleBlock(64, activation="leakyrelu")(var_y)
 </s> remove             var_y = UpscaleBlock(self.encoder_dim)(var_y)
            var_y = UpscaleBlock(self.encoder_dim // 2)(var_y)
            var_y = UpscaleBlock(self.encoder_dim // 4)(var_y)
 </s> add             var_y = UpscaleBlock(self.encoder_dim, activation="leakyrelu")(var_y)
            var_y = UpscaleBlock(self.encoder_dim // 2, activation="leakyrelu")(var_y)
            var_y = UpscaleBlock(self.encoder_dim // 4, activation="leakyrelu")(var_y) </s> remove                 var_y = UpscaleBlock(decoder_complexity)(var_y)
            var_y = UpscaleBlock(decoder_complexity // 2)(var_y)
            var_y = UpscaleBlock(decoder_complexity // 4)(var_y)
 </s> add                 var_y = UpscaleBlock(decoder_complexity, activation="leakyrelu")(var_y)
            var_y = UpscaleBlock(decoder_complexity // 2, activation="leakyrelu")(var_y)
            var_y = UpscaleBlock(decoder_complexity // 4, activation="leakyrelu")(var_y) </s> remove             var_y = UpscaleBlock(512)(var_y)
            var_y = UpscaleBlock(256)(var_y)
            var_y = UpscaleBlock(self.input_shape[0])(var_y)
 </s> add             var_y = UpscaleBlock(512, activation="leakyrelu")(var_y)
            var_y = UpscaleBlock(256, activation="leakyrelu")(var_y)
            var_y = UpscaleBlock(self.input_shape[0], activation="leakyrelu")(var_y)
            var_y = UpscaleBlock(self.decoder_dim * 8, activation="leakyrelu")(var_y)
            var_y = UpscaleBlock(self.decoder_dim * 4, activation="leakyrelu")(var_y)
            var_y = UpscaleBlock(self.decoder_dim * 2, activation="leakyrelu")(var_y)
          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)
            var_y = UpscaleBlock(self.decoder_dim * 2, activation="leakyrelu")(var_y)
            var_y = UpscaleBlock(self.decoder_dim * 8, activation="leakyrelu")(var_y)
            var_y = UpscaleBlock(self.decoder_dim * 4, activation="leakyrelu")(var_y)
            var_y = UpscaleBlock(self.decoder_dim * 2, activation="leakyrelu")(var_y)
            var_y = UpscaleBlock(self.decoder_dim * 8, activation="leakyrelu")(var_y)
            var_y = UpscaleBlock(self.decoder_dim * 4, activation="leakyrelu")(var_y)
            var_y = UpscaleBlock(self.decoder_dim * 2, 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))
  
      def _legacy_mapping(self):
keep keep keep keep replace replace replace keep keep keep keep keep
 lib.model - Maintenance
  - Add Depthwise option to Conv Block
  - Add Swish Activation function
  - remove res_block_follows and add_instance_norm_args
  - Add explicit normalization and activation args
  - Add K.resize_images layer
plugins.train.model - Inference creation bugfix 
plugins/train/model/dfl_sae.py