code: def train():
      model.train()
      total_loss = total_examples = 0
      for head_index, rel_type, tail_index in loader:
          optimizer.zero_grad()
          loss = model.loss(head_index, rel_type, tail_index)
          loss.backward()
          optimizer.step()
          total_loss += float(loss) * head_index.numel()
          total_examples += head_index.numel()
      return total_loss / total_examples
  
imports: import argparse
  import os.path
  import torch
  import torch.optim
  from torch_geometric.datasets import FB15k_237
  from torch_geometric.nn import ComplEx, DistMult, RotatE, TransE
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: unknown