code: def train(
      model: torch.nn.Module,
      optimizer,
      trainloader,
      device,
      curr_epoch,
      total_epochs,
  ):
      model.train()
  
      total_loss = 0.0
      num_batches = len(trainloader)
  
      for batch in trainloader:
          optimizer.zero_grad()
          batch_graphs, batch_labels = batch
          batch_graphs = batch_graphs.to(device)
          batch_labels = batch_labels.long().to(device)
          out, l1, l2 = model(batch_graphs, batch_graphs.ndata["feat"])
          loss = compute_loss(
              out,
              batch_labels,
              l1,
              l2,
              curr_epoch,
              total_epochs,
              device,
          )
          loss.backward()
          optimizer.step()
  
          total_loss += loss.item()
  
      return total_loss / num_batches
  
file_path: dgl/examples/pytorch/gxn/main_early_stop.py
imports: import json
  import os
  from datetime import datetime
  from time import time
  import dgl
  import torch
  import torch.nn.functional
  from data_preprocess import degree_as_feature, node_label_as_feature
  from dgl.data import LegacyTUDataset
  from dgl.dataloading import GraphDataLoader
  from networks import GraphClassifier
  from torch import Tensor
  from torch.utils.data import random_split
  from utils import get_stats, parse_args
label_desc: Use image node_id 2 for calling a global function with example usage: train(model, optimizer, trainloader, device, curr_epoch, total_epochs) and returns: unknown