code: def generate_parameters(self, parameter_id):
          
          if not self.history:
              self.init_search()
  
          new_father_id = None
          generated_graph = None
          if not self.training_queue:
              new_father_id, generated_graph = self.generate()
              new_model_id = self.model_count
              self.model_count += 1
              self.training_queue.append((generated_graph, new_father_id, new_model_id))
              self.descriptors.append(generated_graph.extract_descriptor())
  
          graph, father_id, model_id = self.training_queue.pop(0)
  
          # from graph to json
          json_model_path = os.path.join(self.path, str(model_id) + ".json")
          json_out = graph_to_json(graph, json_model_path)
          self.total_data[parameter_id] = (json_out, father_id, model_id)
  
          return json_out
docs: Returns a set of trial neural architecture, as a serializable object.
  
          Parameters
          ----------
          parameter_id : int