code: def validate(val_loader, model, criterion, args):
      def run_validate(loader, base_progress=0):
          with torch.no_grad():
              end = time.time()
              for i, (images, target) in enumerate(loader):
                  i = base_progress + i
                  if args.gpu is not None:
                      images = images.cuda(args.gpu, non_blocking=True)
                  if torch.cuda.is_available():
                      target = target.cuda(args.gpu, non_blocking=True)
  
                  # compute output
                  output = model(images)
                  loss = criterion(output, target)
                  # measure accuracy and record loss
                  acc1, acc5 = accuracy(output, target, topk=(1, 5))
                  losses.update(loss.clone(), images.size(0))
                  top1.update(acc1[0], images.size(0))
                  top5.update(acc5[0], images.size(0))
  
                  # measure elapsed time
                  batch_time.update(time.time() - end)
                  end = time.time()
  
                  if i % args.print_freq == 0 and args.gpu == 0:
                      progress.display(i + 1)
  
      batch_time = AverageMeter("Time", ":6.3f", Summary.NONE)
      losses = AverageMeter("Loss", ":.4e", Summary.NONE)
      top1 = AverageMeter("Acc@1", ":6.2f", Summary.AVERAGE)
      top5 = AverageMeter("Acc@5", ":6.2f", Summary.AVERAGE)
      progress = ProgressMeter(
          len(val_loader)
          + (
              args.distributed
              and (
                  len(val_loader.sampler) * args.world_size
                  < len(val_loader.dataset)
              )
          ),
          [batch_time, losses, top1, top5],
          prefix="Test: ",
      )
  
      # switch to evaluate mode
      model.eval()
  
      run_validate(val_loader)
      if args.distributed:
          top1.all_reduce()
          top5.all_reduce()
          losses.all_reduce()
  
      if args.distributed and (
          len(val_loader.sampler) * args.world_size
          < len(val_loader.dataset)
      ):
          aux_val_dataset = Subset(
              val_loader.dataset,
              range(
                  len(val_loader.sampler) * args.world_size,
                  len(val_loader.dataset),
              ),
          )
          aux_val_loader = torch.utils.data.DataLoader(
              aux_val_dataset,
              batch_size=args.batch_size,
              shuffle=False,
              num_workers=args.workers,
              pin_memory=True,
          )
          run_validate(aux_val_loader, len(val_loader))
  
      progress.display_summary()
  
      return top5.avg, top1.avg, losses.avg
  
imports: import argparse
  import os
  import random
  import shutil
  import time
  import warnings
  from enum import Enum
  import deepspeed
  from deepspeed.runtime.data_pipeline.data_routing.helper import convert_to_random_ltd, save_without_random_ltd
  import torch
  import torch.nn
  import torch.nn.parallel
  import torch.backends.cudnn
  import torch.distributed
  import torch.optim
  from torch.optim.lr_scheduler import StepLR
  import torch.multiprocessing
  import torch.utils.data
  import torch.utils.data.distributed
  import torchvision.transforms
  import torchvision.datasets
  import torchvision.models
  from torch.utils.data import Subset
  import models
  from models.vit import Block
  import math
  from timm.data.transforms_factory import transforms_imagenet_eval, transforms_imagenet_train
label_desc: Use image node_id 6 for calling a global function with example usage: validate(val_loader, model, criterion, args) and returns: top5,  top1,  losses