code: def validate(val_loader, model, criterion):
      batch_time = AverageMeter()
      losses = AverageMeter()
      top1 = AverageMeter()
      top5 = AverageMeter()
  
      # switch to evaluate mode
      model.eval()
  
      end = time.time()
  
      prefetcher = data_prefetcher(val_loader)
      input, target = prefetcher.next()
      i = -1
      while input is not None:
          i += 1
  
          # compute output
          with torch.no_grad():
              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))
  
          # measure elapsed time
          batch_time.update(time.time() - end)
          end = time.time()
  
          if args.local_rank == 0 and i % args.print_freq == 0:
              print(
                  "Test: [{0}/{1}]\t"
                  "Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t"
                  "Speed {2:.3f} ({3:.3f})\t"
                  "Loss {loss.val:.4f} ({loss.avg:.4f})\t"
                  "Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t"
                  "Prec@5 {top5.val:.3f} ({top5.avg:.3f})".format(
                      i,
                      len(val_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,
                  )
              )
  
          input, target = prefetcher.next()
  
      print(
          " * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}".format(
              top1=top1, top5=top5
          )
      )
  
      return top1.avg
  
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: validate(val_loader, model, criterion) and returns: top1