code: def compute_overlap(z_list):
      ybar_list = [th.max(z, 1)[1] for z in z_list]
      overlap_list = []
      for y_bar in ybar_list:
          accuracy = (
              max(th.sum(y_bar == y).item() for y in y_list)
              / args.n_nodes
          )
          overlap = (accuracy - 1 / K) / (1 - 1 / K)
          overlap_list.append(overlap)
      return sum(overlap_list) / len(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 1 for calling a global function with example usage: compute_overlap(z_list) and returns: unknown