code: def train(model, predictor, loader, optimizer, device):
      model.train()
  
      total_loss = total_examples = 0
      for data in loader:
          data = data.to(device)
          optimizer.zero_grad()
  
          h = model(data.x, data.edge_index)
  
          src, dst = data.edge_index
          pos_out = predictor(h[src], h[dst])
          pos_loss = -torch.log(pos_out + 1e-15).mean()
  
          # Just do some trivial random sampling.
          dst_neg = torch.randint(
              0,
              data.x.size(0),
              src.size(),
              dtype=torch.long,
              device=device,
          )
          neg_out = predictor(h[src], h[dst_neg])
          neg_loss = -torch.log(1 - neg_out + 1e-15).mean()
  
          loss = pos_loss + neg_loss
          loss.backward()
          optimizer.step()
  
          num_examples = src.size(0)
          total_loss += loss.item() * num_examples
          total_examples += num_examples
  
      return total_loss / total_examples
  
imports: import argparse
  import torch
  import numpy
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from torch_sparse import SparseTensor
  from torch_geometric.data import ClusterData, ClusterLoader
  from torch_geometric.nn import GCNConv
  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, loader, optimizer, device) and returns: unknown