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, labels)
  
imports: import itertools
  import os
  import dgl
  import dgl.data
  import numpy
  import scipy.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn import SAGEConv
  import dgl.function
  from sklearn.metrics import roc_auc_score
label_desc: Use image node_id 1 for calling a global function with example usage: compute_loss(pos_score, neg_score) and returns: F