class_docstr: Wrap the model function for dependency injection with gin.
docstr: Run the core of the network, get predictions and loss.
docstr_tok: ["Run","the","core","of","the","network",",","get","predictions","and","loss","."]
code: def call(self, features, training=True):
      """Run the core of the network, get predictions and loss."""
      features = self.encode(features, training=training)
      features.update(self.decoder(features, training=training))
  
      # Run through processor group.
      pg_out = self.processor_group(features, return_outputs_dict=True)
  
      # Parse outputs
      outputs = pg_out["controls"]
      outputs["audio_synth"] = pg_out["signal"]
  
      if training:
          self._update_losses_dict(
              self.loss_objs, features["audio"], outputs["audio_synth"]
          )
  
      return outputs
  
file_path: ddsp/ddsp/training/models/autoencoder.py
imports: import ddsp
  from ddsp.training.models.model import Model
label_desc: Use image node_id 5 for calling the Autoencoder obj's underlying member method code with example usage: obj.call(features, training) and returns: outputs