code: def train(epoch):
      model.train()
      loss_all = 0
  
      for data in train_loader:
          data = data.to(device)
          optimizer.zero_grad()
          loss = F.mse_loss(model(data), data.y)
          loss.backward()
          loss_all += loss.item() * data.num_graphs
          optimizer.step()
      return loss_all / len(train_loader.dataset)
  
file_path: pytorch_geometric/examples/qm9_nn_conv.py
imports: import copy
  import os.path
  import torch
  import torch.nn.functional
  from torch.nn import GRU, Linear, ReLU, Sequential
  import torch_geometric.transforms
  from torch_geometric.datasets import QM9
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import NNConv, Set2Set
  from torch_geometric.utils import remove_self_loops
label_desc: Use image node_id 1 for calling a global function with example usage: train(epoch) and returns: unknown