code: def train(train_loader, model, criterion, scaler, optimizer, epoch):
      batch_time = AverageMeter()
      losses = AverageMeter()
      top1 = AverageMeter()
      top5 = AverageMeter()
  
      # switch to train mode
      model.train()
      end = time.time()
  
      if args.disable_dali:
          data_iterator = data_prefetcher(train_loader)
          data_iterator = iter(data_iterator)
      else:
          data_iterator = train_loader
  
      for i, data in enumerate(data_iterator):
          if args.disable_dali:
              input, target = data
              train_loader_len = len(train_loader)
          else:
              input = data[0]["data"]
              target = data[0]["label"].squeeze(-1).long()
              train_loader_len = int(
                  math.ceil(data_iterator._size / args.batch_size)
              )
  
          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, train_loader_len)
          if args.test:
              if i > 10:
                  break
  
          with torch.cuda.amp.autocast(enabled=args.fp16_mode):
              output = model(input)
              loss = criterion(output, target)
  
          # compute output
          if args.prof >= 0:
              torch.cuda.nvtx.range_push("forward")
  
          if args.prof >= 0:
              torch.cuda.nvtx.range_pop()
  
          # compute gradient and do SGD step
          optimizer.zero_grad()
  
          if args.prof >= 0:
              torch.cuda.nvtx.range_push("backward")
          scaler.scale(loss).backward()
          if args.prof >= 0:
              torch.cuda.nvtx.range_pop()
  
          if args.prof >= 0:
              torch.cuda.nvtx.range_push("optimizer.step()")
          scaler.step(optimizer)
          if args.prof >= 0:
              torch.cuda.nvtx.range_pop()
          scaler.update()
  
          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,
                          train_loader_len,
                          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,
                      )
                  )
  
          # 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()
  
      return batch_time.avg
  
imports: import argparse
  import os
  import shutil
  import time
  import math
  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 numpy
  from torch.nn.parallel import DistributedDataParallel
  import torchvision.transforms
  import torchvision.datasets
  import torchvision.models
label_desc: Use image node_id 6 for calling a global function with example usage: train(train_loader, model, criterion, scaler, optimizer, epoch) and returns: batch_time