code: def merge_result(outputs):
      graphs = []
      anchor_eids = []
      dists_max_list = []
      edge_weights = []
  
      for g, anchor_eid, dists_max, edge_weight in outputs:
          graphs.extend(g)
          anchor_eids.extend(anchor_eid)
          dists_max_list.extend(dists_max)
          edge_weights.extend(edge_weight)
  
      return graphs, anchor_eids, dists_max_list, edge_weights
  
imports: import multiprocessing
  import random
  from multiprocessing import get_context
  import networkx
  import numpy
  import torch
  from tqdm.auto import tqdm
label_desc: Use image node_id 16 for calling a global function with example usage: merge_result(outputs) and returns: graphs,  anchor_eids,  dists_max_list,  edge_weights