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()
  
      if args.disable_dali:
          data_iterator = data_prefetcher(val_loader)
          data_iterator = iter(data_iterator)
      else:
          data_iterator = val_loader
  
      for i, data in enumerate(data_iterator):
          if args.disable_dali:
              input, target = data
              val_loader_len = len(val_loader)
          else:
              input = data[0]["data"]
              target = data[0]["label"].squeeze(-1).long()
              val_loader_len = int(
                  math.ceil(data_iterator._size / args.batch_size)
              )
  
          # 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()
  
          # TODO:  Change timings to mirror train().
          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,
                      val_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,
                  )
              )
  
      print(
          " * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}".format(
              top1=top1, top5=top5
          )
      )
  
      return [top1.avg, top5.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 7 for calling a global function with example usage: validate(val_loader, model, criterion) and returns: list