code: def train(
      train_loader, model, criterion, optimizer, epoch, print_freq=10
  ):
      batch_time = AverageMeter()
      losses = AverageMeter()
      top1 = AverageMeter()
      top5 = AverageMeter()
  
      # switch to train mode
      model.train()
  
      end = time.time()
  
      for i, (input, target) in enumerate(train_loader):
          # compute output
          output = model(input)
          loss = criterion(output, target)
  
          # measure accuracy and record loss
          prec1, prec5 = accuracy(output, target, topk=(1, 5))
          losses.add(loss.item(), input.size(0))
          top1.add(prec1[0], input.size(0))
          top5.add(prec5[0], input.size(0))
  
          # compute gradient and do SGD step
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
  
          # measure elapsed time
          current_batch_time = time.time() - end
          batch_time.add(current_batch_time)
          end = time.time()
  
          if i % print_freq == 0:
              logging.info(
                  "Epoch: [{}][{}/{}]\t"
                  "Time {:.3f} ({:.3f})\t"
                  "Loss {:.4f} ({:.4f})\t"
                  "Prec@1 {:.3f} ({:.3f})\t"
                  "Prec@5 {:.3f} ({:.3f})".format(
                      epoch,
                      i,
                      len(train_loader),
                      current_batch_time,
                      batch_time.value(),
                      loss.item(),
                      losses.value(),
                      prec1[0],
                      top1.value(),
                      prec5[0],
                      top5.value(),
                  )
              )
  
imports: import logging
  import os
  import random
  import shutil
  import tempfile
  import time
  import crypten
  import crypten.communicator
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import torch.utils.data
  import torch.utils.data.distributed
  from examples.meters import AverageMeter
  from examples.util import NoopContextManager
  from torchvision import datasets, transforms
label_desc: Use image node_id 2 for calling a global function with example usage: train(train_loader, model, criterion, optimizer, epoch, print_freq) without return types