code: def train(epoch):
      model.train()
  
      total_loss = 0
      for data in train_loader:
          data = data.to(device)
          optimizer.zero_grad()
          out = model(
              data.x, data.edge_index, data.edge_attr, data.batch
          )
          loss = (out.squeeze() - data.y).abs().mean()
          loss.backward()
          total_loss += loss.item() * data.num_graphs
          optimizer.step()
      return total_loss / len(train_loader.dataset)
  
file_path: pytorch_geometric/examples/pna.py
imports: import os.path
  import torch
  import torch.nn.functional
  from torch.nn import Embedding, Linear, ModuleList, ReLU, Sequential
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  from torch_geometric.datasets import ZINC
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import BatchNorm, PNAConv, global_add_pool
  from torch_geometric.utils import degree
label_desc: Use image node_id 1 for calling a global function with example usage: train(epoch) and returns: unknown