          input_shape = self.state.face_shapes[0]
          logger.debug("Setting input shape from state file: %s", input_shape)
          self.input_shape = input_shape
  
      def add_network(self, network_type, side, network):
          """ Add a NNMeta object """
          logger.debug("network_type: '%s', side: '%s', network: '%s'", network_type, side, network)
          filename = "{}_{}".format(self.name, network_type.lower())
          name = network_type.lower()
          if side:
 
</s> remove         logger.debug("network_type: '%s', side: '%s', network: '%s'", network_type, side, network)
 </s> add         logger.debug("network_type: '%s', side: '%s', network: '%s', is_output: %s",
                     network_type, side, network, is_output) </s> remove     def __init__(self, filename, network_type, side, network):
 </s> add     def __init__(self, filename, network_type, side, network, is_output): </s> remove                      "network: %s", self.__class__.__name__, filename, network_type,
                     side, network)
 </s> add                      "network: %s, is_output: %s", self.__class__.__name__, filename,
                     network_type, side, network, is_output) </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     network:      Define network to this.
 </s> add     network:    Define network to this.
    is_output:  Set to True to indicate that this network is an output to the Autoencoder
    def add_network(self, network_type, side, network, is_output=False):
          input_shape = self.state.face_shapes[0]
          logger.debug("Setting input shape from state file: %s", input_shape)
          self.input_shape = input_shape
  
    def add_network(self, network_type, side, network, is_output=False):
          """ Add a NNMeta object """
          logger.debug("network_type: '%s', side: '%s', network: '%s'", network_type, side, network)
          filename = "{}_{}".format(self.name, network_type.lower())
          name = network_type.lower()
          if side:
keep keep keep keep 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/_base.py