code: def train(model, optimizer, data):
      model.train()
      optimizer.zero_grad()
      out = model(data)
      loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
      loss.backward()
      optimizer.step()
  
file_path: pytorch_geometric/benchmark/citation/train_eval.py
imports: import time
  import torch
  import torch.nn.functional
  from torch import tensor
  from torch.optim import Adam
  from torch_geometric.profile import timeit, torch_profile
  from torch_geometric.utils import index_to_mask
label_desc: Use image node_id 5 for calling a global function with example usage: train(model, optimizer, data) without return types