          var_x = Conv2DBlock(512)(var_x)
          var_x = Dense(self.encoder_dim)(Flatten()(var_x))
          var_x = Dense(4 * 4 * 512)(var_x)
          var_x = Reshape((4, 4, 512))(var_x)
          var_x = UpscaleBlock(256)(var_x)
          return KerasModel(input_, var_x, name="encoder")
  
      def decoder(self, side):
          """ Decoder Network """
          input_ = Input(shape=(8, 8, 256))
 
</s> remove         var_x = Conv2DBlock(128)(var_x)
        var_x = Conv2DBlock(256)(var_x)
        var_x = Conv2DBlock(512)(var_x)
 </s> add         var_x = Conv2DBlock(128, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(256, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(512, activation="leakyrelu")(var_x)
 </s> remove         var_x = UpscaleBlock(512)(var_x)
 </s> add         var_x = UpscaleBlock(512, activation="leakyrelu")(var_x)
 </s> remove             var_x = Conv2DBlock(1024)(var_x)
 </s> add             var_x = Conv2DBlock(1024, activation="leakyrelu")(var_x)
 </s> remove         var_x = UpscaleBlock(self.encoder_dim)(var_x)
 </s> add         var_x = UpscaleBlock(self.encoder_dim, activation="leakyrelu")(var_x) </s> remove         var_x = UpscaleBlock(512)(var_x)
        var_x = UpscaleBlock(256)(var_x)
        var_x = UpscaleBlock(128)(var_x)
 </s> add         var_x = UpscaleBlock(512, activation="leakyrelu")(var_x)
        var_x = UpscaleBlock(256, activation="leakyrelu")(var_x)
        var_x = UpscaleBlock(128, activation="leakyrelu")(var_x)

        var_x = UpscaleBlock(256, activation="leakyrelu")(var_x)

          var_x = Conv2DBlock(512)(var_x)
          var_x = Dense(self.encoder_dim)(Flatten()(var_x))
          var_x = Dense(4 * 4 * 512)(var_x)
          var_x = Reshape((4, 4, 512))(var_x)
        var_x = UpscaleBlock(256, activation="leakyrelu")(var_x)
          return KerasModel(input_, var_x, name="encoder")
  
      def decoder(self, side):
          """ Decoder Network """
          input_ = Input(shape=(8, 8, 256))
keep keep keep keep 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/lightweight.py