code: def train():
      model.train()
  
      neg_edge_index = negative_sampling(
          edge_index=data.train_pos_edge_index,  # positive edges
          num_nodes=data.num_nodes,  # number of nodes
          num_neg_samples=data.train_pos_edge_index.size(1),
      ).to(
          device
      )  # number of neg_sample equal to number of pos_edges
  
      optimizer.zero_grad()
      z = model.lp_encode(data)
      link_logits = model.lp_decode(
          z, data.train_pos_edge_index, neg_edge_index
      )
      link_labels = get_link_labels(
          data.train_pos_edge_index, neg_edge_index
      )
      loss = F.binary_cross_entropy_with_logits(
          link_logits, link_labels
      )
      loss.backward()
      optimizer.step()
  
      return loss
  
imports: import os
  import time
  import torch
  import torch.nn.functional
  import numpy
  import os.path
  import random
  from torch_geometric.datasets import Planetoid
  from torch_geometric.data import Data
  from autogl.module.model.encoders import GCNEncoderMaintainer, GATEncoderMaintainer, SAGEEncoderMaintainer
  from autogl.module.model.decoders import DotProductLinkPredictionDecoderMaintainer
  import torch_geometric.transforms
  from torch_geometric.utils import train_test_split_edges
  from torch_geometric.utils import negative_sampling
  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: train() and returns: loss