  
      def add_networks(self):
          """ Add the original model weights """
          logger.debug("Adding networks")
          self.add_network("decoder", "a", self.decoder_a())
          self.add_network("decoder", "b", self.decoder_b())
          self.add_network("encoder", None, self.encoder())
          logger.debug("Added networks")
  
      def encoder(self):
          """ Unbalanced Encoder """
 
</s> remove         self.add_network("decoder", "a", self.decoder_a())
        self.add_network("decoder", "b", self.decoder_b())
 </s> add         self.add_network("decoder", "a", self.decoder_a(), is_output=True)
        self.add_network("decoder", "b", self.decoder_b(), is_output=True) </s> remove         self.add_network("decoder", "a", self.decoder())
        self.add_network("decoder", "b", self.decoder())
 </s> add         self.add_network("decoder", "a", self.decoder(), is_output=True)
        self.add_network("decoder", "b", self.decoder(), is_output=True)
 </s> remove         self.networks[name] = NNMeta(str(self.model_dir / filename), network_type, side, network)
 </s> add         self.networks[name] = NNMeta(str(self.model_dir / filename),
                                     network_type,
                                     side,
                                     network,
                                     is_output) </s> remove from keras.layers import Conv2D, Dense, Flatten, Input, Reshape
 </s> add from keras.layers import Dense, Flatten, Input, Reshape

        self.add_network("decoder", "a", self.decoder_a(), is_output=True)
        self.add_network("decoder", "b", self.decoder_b(), is_output=True)
  
      def add_networks(self):
          """ Add the original model weights """
          logger.debug("Adding networks")
        self.add_network("decoder", "a", self.decoder_a(), is_output=True)
        self.add_network("decoder", "b", self.decoder_b(), is_output=True)
        self.add_network("decoder", "a", self.decoder_a(), is_output=True)
        self.add_network("decoder", "b", self.decoder_b(), is_output=True)
          self.add_network("encoder", None, self.encoder())
          logger.debug("Added networks")
  
      def encoder(self):
          """ Unbalanced Encoder """
keep keep keep keep replace replace keep keep keep keep keep
 Add Convolutional Aware Initialization (#795)

* Training: Add Convolutional Aware Initialization config option

* Centralize Conv2D layer for handling initializer

* Add 'is-output' to NNMeta to indicate that network is an output to the Model 
plugins/train/model/unbalanced.py