code: def train(model, dataloader):
      model.train()
  
      for data in dataloader:
          data = data.to(device)
          optimizer.zero_grad()
          F.nll_loss(model(data.x, data), data.y).backward()
          optimizer.step()
  
file_path: pytorch_geometric/examples/contrib/pgm_explainer_graph_classification.py
imports: import os.path
  import torch
  import torch.nn.functional
  from torch.nn import Linear, ReLU, Sequential
  import torch_geometric.transforms
  from torch_geometric.contrib.explain import PGMExplainer
  from torch_geometric.datasets import MNISTSuperpixels
  from torch_geometric.explain import Explainer
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import NNConv, global_mean_pool, graclus, max_pool, max_pool_x
  from torch_geometric.utils import normalized_cut
label_desc: Use image node_id 2 for calling a global function with example usage: train(model, dataloader) without return types