code: def run_cna(graph, root, targets, relationship_dict=None):
      
      causal_effects = []
  
      relationship_dict = causal_effect_dict if relationship_dict is None else relationship_dict
  
      for target in targets:
          try:
              shortest_paths = nx.all_shortest_paths(graph, source=root, target=target)
  
              effects_in_path = set()
  
              for shortest_path in shortest_paths:
                  effects_in_path.add(get_path_effect(graph, shortest_path, relationship_dict))
  
              if len(effects_in_path) == 1:
                  causal_effects.append((root, target, next(iter(effects_in_path))))  # Append the only predicted effect
  
              elif Effect.activation in effects_in_path and Effect.inhibition in effects_in_path:
                  causal_effects.append((root, target, Effect.ambiguous))
  
              elif Effect.activation in effects_in_path and Effect.inhibition not in effects_in_path:
                  causal_effects.append((root, target, Effect.activation))
  
              elif Effect.inhibition in effects_in_path and Effect.activation not in effects_in_path:
                  causal_effects.append((root, target, Effect.inhibition))
  
              else:
                  log.warning('Exception in set: {}.'.format(effects_in_path))
  
          except nx.NetworkXNoPath:
              log.warning('No shortest path between: {} and {}.'.format(root, target))
  
      return causal_effects
docs: Returns the effect from the root to the target nodes represented as {-1,1}
  
      :param pybel.BELGraph graph: A BEL graph
      :param BaseEntity root: The root node
      :param iter targets: The targets nodes
      :param dict relationship_dict: dictionary with relationship effects
      :return list[tuple]: