code: def get_relevant_nodes(network_id, query_nodes):
      
      url = ndex_relevance + '/rank_entities'
      kernel_id = get_heat_kernel(network_id)
      if kernel_id is None:
          return None
      if isinstance(query_nodes, basestring):
          query_nodes = [query_nodes]
      params = {'identifier_set': query_nodes,
                'kernel_id': kernel_id}
      res = ndex_client.send_request(url, params, is_json=True)
      if res is None:
          logger.error("ndex_client.send_request returned None.")
          return None
      ranked_entities = res.get('ranked_entities')
      if ranked_entities is None:
          logger.error('Could not get ranked entities.')
          return None
      return ranked_entities
docs: Return a set of network nodes relevant to a given query set.
  
      A heat diffusion algorithm is used on a pre-computed heat kernel for the
      given network which starts from the given query nodes. The nodes
      in the network are ranked according to heat score which is a measure
      of relevance with respect to the query nodes.
  
      Parameters
      ----------
      network_id : str
          The UUID of the network in NDEx.
      query_nodes : list[str]
          A list of node names with respect to which relevance is queried.
  
      Returns
      -------
      ranked_entities : list[(str, float)]
          A list containing pairs of node names and their relevance scores.