code: def train(
      model, predictor, x, pos_loader, neg_loader, optimizer, device
  ):
      model.train()
  
      pbar = tqdm(total=1000)
      pbar.set_description("Training")
  
      total_loss = total_examples = 0
      for i, (pos_data, neg_data) in enumerate(
          zip(pos_loader, neg_loader)
      ):
          optimizer.zero_grad()
  
          batch_size, n_id, adjs = pos_data
          adjs = [adj.to(device) for adj in adjs]
          h = model(x[n_id], adjs)
          h_src, h_dst = h.chunk(2, dim=0)
          pos_out = predictor(h_src, h_dst)
          pos_loss = -torch.log(pos_out + 1e-15).mean()
  
          batch_size, n_id, adjs = neg_data
          adjs = [adj.to(device) for adj in adjs]
          h = model(x[n_id], adjs)
          h_src, h_dst = h.chunk(2, dim=0)
          neg_out = predictor(h_src, h_dst)
          neg_loss = -torch.log(1 - neg_out + 1e-15).mean()
  
          loss = pos_loss + neg_loss
          loss.backward()
          optimizer.step()
  
          num_examples = h_src.size(0)
          total_loss += loss.item() * num_examples
          total_examples += num_examples
  
          pbar.update(1)
  
          # We don't run a full epoch here since that just takes too much time.
          if (i + 1) % 1000 == 0:
              break
  
      pbar.close()
  
      return total_loss / total_examples
  
imports: import argparse
  from tqdm.auto import tqdm
  import torch
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from torch_geometric.data import NeighborSampler
  from torch_geometric.nn import SAGEConv
  from torch_geometric.utils import to_undirected
  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(model, predictor, x, pos_loader, neg_loader, optimizer, device) and returns: unknown