code: def train(model, optimizer, loader):
      model.train()
  
      total_loss = 0
      for data in loader:
          optimizer.zero_grad()
          data = data.to(device)
          out = model(data)
          loss = F.nll_loss(out, data.y.view(-1))
          loss.backward()
          total_loss += loss.item() * num_graphs(data)
          optimizer.step()
      return total_loss / len(loader.dataset)
  
file_path: pytorch_geometric/benchmark/kernel/train_eval.py
imports: import time
  import torch
  import torch.nn.functional
  from sklearn.model_selection import StratifiedKFold
  from torch import tensor
  from torch.optim import Adam
  from torch_geometric.loader import DataLoader
  from torch_geometric.loader import DenseDataLoader
label_desc: Use image node_id 4 for calling a global function with example usage: train(model, optimizer, loader) and returns: unknown