code: def train(model, device, data_loader, opt, loss_fn):
      model.train()
  
      train_loss = []
      for g, labels in data_loader:
          g = g.to(device)
          labels = labels.to(torch.float32).to(device)
          logits = model(g, g.edata["feat"], g.ndata["feat"])
          loss = loss_fn(logits, labels)
          train_loss.append(loss.item())
  
          opt.zero_grad()
          loss.backward()
          opt.step()
  
      return sum(train_loss) / len(train_loss)
  
file_path: dgl/examples/pytorch/deepergcn/main.py
imports: import argparse
  import copy
  import time
  import torch
  import torch.nn
  import torch.optim
  from models import DeeperGCN
  from ogb.graphproppred import collate_dgl, DglGraphPropPredDataset, Evaluator
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 for calling a global function with example usage: train(model, device, data_loader, opt, loss_fn) and returns: unknown