code: def train(args, model, device, train_loader, optimizer, epoch):
      model.train()
      for batch_idx, (data, target) in enumerate(train_loader):
          data, target = data.to(device), target.to(device)
          optimizer.zero_grad()
          output = model(data)
          loss = F.nll_loss(output, target)
          loss.backward()
          optimizer.step()
          if batch_idx % args.log_interval == 0:
              msg = "Train Epoch: {} [{}/{} ({:.0f}%)]\tloss={:.4f}".format(
                  epoch,
                  batch_idx,
                  len(train_loader),
                  100.0 * batch_idx / len(train_loader),
                  loss.item(),
              )
              logging.info(msg)
              niter = epoch * len(train_loader) + batch_idx
  
imports: from __future__ import print_function
  import argparse
  import logging
  import os
  import time
  from torchvision import datasets, transforms
  from torch.utils.data.distributed import DistributedSampler
  import torch
  import torch.distributed
  import torch.nn
  import torch.nn.functional
  import torch.optim
label_desc: Use image node_id 1 for calling a global function with example usage: train(args, model, device, train_loader, optimizer, epoch) without return types