code: def train():
      model.train()
  
      total_loss = 0
      for data in train_loader:
          data = data.to(device)
          optimizer.zero_grad()
          out = model(data)
          loss = F.nll_loss(out, data.y)
          loss.backward()
          total_loss += loss.item() * data.num_graphs
          optimizer.step()
      return total_loss / len(train_dataset)
  
file_path: pytorch_geometric/examples/dgcnn_classification.py
imports: import os.path
  import torch
  import torch.nn.functional
  from torch.nn import Linear
  import torch_geometric.transforms
  from torch_geometric.datasets import ModelNet
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import MLP, DynamicEdgeConv, global_max_pool
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: unknown