code: def test():
      p_list = [6, 5.5, 5, 4.5, 1.5, 1, 0.5, 0]
      q_list = [0, 0.5, 1, 1.5, 4.5, 5, 5.5, 6]
      N = 1
      overlap_list = []
      for p, q in zip(p_list, q_list):
          dataset = SBMMixtureDataset(
              N, args.n_nodes, K, pq=[[p, q]] * N
          )
          loader = DataLoader(dataset, N, collate_fn=dataset.collate_fn)
          g, lg, deg_g, deg_lg, pm_pd = next(iter(loader))
          z = inference(g, lg, deg_g, deg_lg, pm_pd)
          overlap_list.append(compute_overlap(th.chunk(z, N, 0)))
      return overlap_list
  
file_path: dgl/examples/pytorch/line_graph/train.py
imports: from __future__ import division
  import argparse
  import time
  from itertools import permutations
  import gnn
  import numpy
  import torch
  import torch.nn.functional
  import torch.optim
  from dgl.data import SBMMixtureDataset
  from torch.utils.data import DataLoader
label_desc: Use image node_id 5 for calling a global function with example usage: test() and returns: overlap_list