code: def train(predictor, x, split_edge, optimizer, batch_size):
      predictor.train()
  
      pos_train_edge = split_edge["train"]["edge"].to(x.device)
  
      neg_train_edge = split_edge["train"]["edge_neg"].to(
          x.device
      )  # modified
  
      total_loss = total_examples = 0
      for perm in DataLoader(
          range(pos_train_edge.size(0)), batch_size, shuffle=True
      ):
          optimizer.zero_grad()
  
          edge = pos_train_edge[perm].t()
  
          pos_out = predictor(x[edge[0]], x[edge[1]])
          pos_loss = -torch.log(pos_out + 1e-15).mean()
  
          # random element of previously sampled negative edges
          # negative samples are obtained by using spatial sampling criteria
          edge = neg_train_edge[perm].t()
          neg_out = predictor(x[edge[0]], x[edge[1]])
          neg_loss = -torch.log(1 - neg_out + 1e-15).mean()
  
          loss = pos_loss + neg_loss
          loss.backward()
          optimizer.step()
  
          num_examples = pos_out.size(0)
          total_loss += loss.item() * num_examples
          total_examples += num_examples
  
      return total_loss / total_examples
  
file_path: ogb/examples/linkproppred/vessel/mlp.py
imports: import argparse
  import torch
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from ogb.linkproppred import PygLinkPropPredDataset, Evaluator
  from logger import Logger
label_desc: Use image node_id 1 for calling a global function with example usage: train(predictor, x, split_edge, optimizer, batch_size) and returns: unknown