code: def train(train_loader, model, criterion, optimizer, epoch):
      batch_time = AverageMeter()
      losses = AverageMeter()
      top1 = AverageMeter()
      top5 = AverageMeter()
  
      # switch to train mode
      model.train()
      end = time.time()
  
      prefetcher = data_prefetcher(train_loader)
      input, target = prefetcher.next()
      i = 0
      while input is not None:
          i += 1
          if args.prof >= 0 and i == args.prof:
              print("Profiling begun at iteration {}".format(i))
              torch.cuda.cudart().cudaProfilerStart()
  
          if args.prof >= 0:
              torch.cuda.nvtx.range_push(
                  "Body of iteration {}".format(i)
              )
  
          adjust_learning_rate(optimizer, epoch, i, len(train_loader))
  
          # compute output
          if args.prof >= 0:
              torch.cuda.nvtx.range_push("forward")
          output = model(input)
          if args.prof >= 0:
              torch.cuda.nvtx.range_pop()
          loss = criterion(output, target)
  
          # compute gradient and do SGD step
          optimizer.zero_grad()
  
          if args.prof >= 0:
              torch.cuda.nvtx.range_push("backward")
          with amp.scale_loss(loss, optimizer) as scaled_loss:
              scaled_loss.backward()
          if args.prof >= 0:
              torch.cuda.nvtx.range_pop()
  
          # for param in model.parameters():
          #     print(param.data.double().sum().item(), param.grad.data.double().sum().item())
  
          if args.prof >= 0:
              torch.cuda.nvtx.range_push("optimizer.step()")
          optimizer.step()
          if args.prof >= 0:
              torch.cuda.nvtx.range_pop()
  
          if i % args.print_freq == 0:
              # Every print_freq iterations, check the loss, accuracy, and speed.
              # For best performance, it doesn't make sense to print these metrics every
              # iteration, since they incur an allreduce and some host<->device syncs.
  
              # Measure accuracy
              prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
  
              # Average loss and accuracy across processes for logging
              if args.distributed:
                  reduced_loss = reduce_tensor(loss.data)
                  prec1 = reduce_tensor(prec1)
                  prec5 = reduce_tensor(prec5)
              else:
                  reduced_loss = loss.data
  
              # to_python_float incurs a host<->device sync
              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))
  
              torch.cuda.synchronize()
              batch_time.update((time.time() - end) / args.print_freq)
              end = time.time()
  
              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"
                      "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,
                          loss=losses,
                          top1=top1,
                          top5=top5,
                      )
                  )
          if args.prof >= 0:
              torch.cuda.nvtx.range_push("prefetcher.next()")
          input, target = prefetcher.next()
          if args.prof >= 0:
              torch.cuda.nvtx.range_pop()
  
          # Pop range "Body of iteration {}".format(i)
          if args.prof >= 0:
              torch.cuda.nvtx.range_pop()
  
          if args.prof >= 0 and i == args.prof + 10:
              print("Profiling ended at iteration {}".format(i))
              torch.cuda.cudart().cudaProfilerStop()
              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 4 for calling a global function with example usage: train(train_loader, model, criterion, optimizer, epoch) without return types