code: def workflow_aggregate(graph: BELGraph,
                         node: BaseEntity,
                         key: Optional[str] = None,
                         tag: Optional[str] = None,
                         default_score: Optional[float] = None,
                         runs: Optional[int] = None,
                         aggregator: Optional[Callable[[Iterable[float]], float]] = None,
                         ) -> Optional[float]:
      
      runners = workflow(graph, node, key=key, tag=tag, default_score=default_score, runs=runs)
      scores = [runner.get_final_score() for runner in runners]
  
      if not scores:
          log.warning('Unable to run the heat diffusion workflow for %s', node)
          return
  
      if aggregator is None:
          return np.average(scores)
  
      return aggregator(scores)
docs: Get the average score over multiple runs.
  
      This function is very simple, and can be copied to do more interesting statistics over the :class:`Runner`
      instances. To iterate over the runners themselves, see :func:`workflow`
  
      :param graph: A BEL graph
      :param node: The BEL node that is the focus of this analysis
      :param key: The key in the node data dictionary representing the experimental data. Defaults to
       :data:`pybel_tools.constants.WEIGHT`.
      :param tag: The key for the nodes' data dictionaries where the scores will be put. Defaults to 'score'
      :param default_score: The initial score for all nodes. This number can go up or down.
      :param runs: The number of times to run the heat diffusion workflow. Defaults to 100.
      :param aggregator: A function that aggregates a list of scores. Defaults to :func:`numpy.average`.
                         Could also use: :func:`numpy.mean`, :func:`numpy.median`, :func:`numpy.min`, :func:`numpy.max`
      :return: The average score for the target node