code: def train(train_loader, model, criterion, optimizer, epoch):
      batch_time = AverageMeter()
      data_time = AverageMeter()
      losses = AverageMeter()
      top1 = AverageMeter()
      top5 = AverageMeter()
  
      # switch to train mode
      model.train()
      end = time.time()
  
      run_info_dict = {"Iteration": [], "Loss": [], "Speed": []}
  
      prefetcher = data_prefetcher(train_loader)
      input, target = prefetcher.next()
      i = -1
      while input is not None:
          i += 1
  
          # No learning rate warmup for this test, to expose bitwise inaccuracies more quickly
          # adjust_learning_rate(optimizer, epoch, i, len(train_loader))
  
          if args.prof:
              if i > 10:
                  break
          # measure data loading time
          data_time.update(time.time() - end)
  
          # compute output
          output = model(input)
          loss = criterion(output, target)
  
          # measure accuracy and record loss
          prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
  
          if args.distributed:
              reduced_loss = reduce_tensor(loss.data)
              prec1 = reduce_tensor(prec1)
              prec5 = reduce_tensor(prec5)
          else:
              reduced_loss = loss.data
  
          losses.update(to_python_float(reduced_loss), input.size(0))
          top1.update(to_python_float(prec1), input.size(0))
          top5.update(to_python_float(prec5), input.size(0))
  
          # compute gradient and do SGD step
          optimizer.zero_grad()
  
          with amp.scale_loss(loss, optimizer) as scaled_loss:
              scaled_loss.backward()
  
          # for param in model.parameters():
          #     print(param.data.double().sum().item(), param.grad.data.double().sum().item())
  
          # torch.cuda.synchronize()
          torch.cuda.nvtx.range_push("step")
          optimizer.step()
          torch.cuda.nvtx.range_pop()
  
          torch.cuda.synchronize()
          # measure elapsed time
          batch_time.update(time.time() - end)
  
          end = time.time()
  
          # If you decide to refactor this test, like examples/imagenet, to sample the loss every
          # print_freq iterations, make sure to move this prefetching below the accuracy calculation.
          input, target = prefetcher.next()
  
          if i % args.print_freq == 0 and i > 1:
              if args.local_rank == 0:
                  print(
                      "Epoch: [{0}][{1}/{2}]\t"
                      "Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t"
                      "Speed {3:.3f} ({4:.3f})\t"
                      "Data {data_time.val:.3f} ({data_time.avg:.3f})\t"
                      "Loss {loss.val:.10f} ({loss.avg:.4f})\t"
                      "Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t"
                      "Prec@5 {top5.val:.3f} ({top5.avg:.3f})".format(
                          epoch,
                          i,
                          len(train_loader),
                          args.world_size
                          * args.batch_size
                          / batch_time.val,
                          args.world_size
                          * args.batch_size
                          / batch_time.avg,
                          batch_time=batch_time,
                          data_time=data_time,
                          loss=losses,
                          top1=top1,
                          top5=top5,
                      )
                  )
              run_info_dict["Iteration"].append(i)
              run_info_dict["Loss"].append(losses.val)
              run_info_dict["Speed"].append(
                  args.world_size * args.batch_size / batch_time.val
              )
              if len(run_info_dict["Loss"]) == args.prints_to_process:
                  if args.local_rank == 0:
                      torch.save(
                          run_info_dict,
                          str(args.has_ext)
                          + "_"
                          + str(args.opt_level)
                          + "_"
                          + str(args.loss_scale)
                          + "_"
                          + str(args.keep_batchnorm_fp32)
                          + "_"
                          + str(args.fused_adam),
                      )
                  quit()
  
imports: import argparse
  import os
  import shutil
  import time
  import torch
  import torch.nn
  import torch.nn.parallel
  import torch.backends.cudnn
  import torch.distributed
  import torch.optim
  import torch.utils.data
  import torch.utils.data.distributed
  import torchvision.transforms
  import torchvision.datasets
  import torchvision.models
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: train(train_loader, model, criterion, optimizer, epoch) without return types