code: def forward(self, block_outputs, pos_graph, neg_graph):
      with pos_graph.local_scope():
          pos_graph.ndata["h"] = block_outputs
          pos_graph.apply_edges(fn.u_dot_v("h", "h", "score"))
          pos_score = pos_graph.edata["score"]
      with neg_graph.local_scope():
          neg_graph.ndata["h"] = block_outputs
          neg_graph.apply_edges(fn.u_dot_v("h", "h", "score"))
          neg_score = neg_graph.edata["score"]
  
      score = torch.cat([pos_score, neg_score])
      label = torch.cat(
          [torch.ones_like(pos_score), torch.zeros_like(neg_score)]
      ).long()
      loss = F.binary_cross_entropy_with_logits(score, label.float())
      return loss
  
imports: import argparse
  import os
  import time
  import dgl.function
  import dgl.nn
  import numpy
  import sklearn.linear_model
  import sklearn.metrics
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import tqdm
  from dgl.data import AsNodePredDataset, RedditDataset
  from dgl.dataloading import as_edge_prediction_sampler, DataLoader, MultiLayerFullNeighborSampler, NeighborSampler
  from dgl.multiprocessing import shared_tensor
  from ogb.nodeproppred import DglNodePropPredDataset
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 1 for calling the CrossEntropyLoss obj's underlying member method code with example usage: obj.forward(block_outputs, pos_graph, neg_graph) and returns: loss