code: def parallel_graph_evaluation(data, adj_matrix, nb_runs=16,
                                nb_jobs=None, **kwargs):
      
      nb_jobs = SETTINGS.get_default(nb_jobs=nb_jobs)
      if nb_runs == 1:
          return graph_evaluation(data, adj_matrix, **kwargs)
      else:
          output = Parallel(n_jobs=nb_jobs)(delayed(graph_evaluation)(data, adj_matrix,
                                            idx=run, gpu_id=run % SETTINGS.GPU,
                                            **kwargs) for run in range(nb_runs))
          return np.mean(output)
docs: Parallelize the various runs of CGNN to evaluate a graph.