code: def compute_loss(pos_score, neg_score):
      scores = torch.cat([pos_score, neg_score])
      labels = torch.cat(
          [
              torch.ones(pos_score.shape[0]),
              torch.zeros(neg_score.shape[0]),
          ]
      )
      return F.binary_cross_entropy_with_logits(scores.cpu(), labels)
  
imports: import dgl
  import torch
  import pickle
  import torch.nn
  import torch.nn.functional
  import numpy
  import scipy.sparse
  import dgl.function
  import random
  from dgl.data import CoraGraphDataset, PubmedGraphDataset, CiteseerGraphDataset
  from autogl.module.model.dgl import AutoSAGE, AutoGCN, AutoGAT
  import dgl.data
  from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
  from tqdm import tqdm
  from helper import get_encoder_decoder_hp
  from sklearn.metrics import roc_auc_score
label_desc: Use image node_id 3 for calling a global function with example usage: compute_loss(pos_score, neg_score) and returns: F