code: def validate(val_loader, model, criterion, print_freq=10):
      batch_time = AverageMeter()
      losses = AverageMeter()
      top1 = AverageMeter()
      top5 = AverageMeter()
  
      # switch to evaluate mode
      model.eval()
  
      with torch.no_grad():
          end = time.time()
          for i, (input, target) in enumerate(val_loader):
              if isinstance(
                  model, crypten.nn.Module
              ) and not crypten.is_encrypted_tensor(input):
                  input = encrypt_data_tensor_with_src(input)
              # compute output
              output = model(input)
              if crypten.is_encrypted_tensor(output):
                  output = output.get_plain_text()
              loss = criterion(output, target)
  
              # measure accuracy and record loss
              prec1, prec5 = accuracy(output, target, topk=(1, 5))
              losses.add(loss.item(), input.size(0))
              top1.add(prec1[0], input.size(0))
              top5.add(prec5[0], input.size(0))
  
              # measure elapsed time
              current_batch_time = time.time() - end
              batch_time.add(current_batch_time)
              end = time.time()
  
              if (i + 1) % print_freq == 0:
                  logging.info(
                      "\nTest: [{}/{}]\t"
                      "Time {:.3f} ({:.3f})\t"
                      "Loss {:.4f} ({:.4f})\t"
                      "Prec@1 {:.3f} ({:.3f})   \t"
                      "Prec@5 {:.3f} ({:.3f})".format(
                          i + 1,
                          len(val_loader),
                          current_batch_time,
                          batch_time.value(),
                          loss.item(),
                          losses.value(),
                          prec1[0],
                          top1.value(),
                          prec5[0],
                          top5.value(),
                      )
                  )
  
          logging.info(
              " * Prec@1 {:.3f} Prec@5 {:.3f}".format(
                  top1.value(), top5.value()
              )
          )
      return top1.value()
  
imports: import logging
  import os
  import random
  import shutil
  import tempfile
  import time
  import crypten
  import crypten.communicator
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import torch.utils.data
  import torch.utils.data.distributed
  from examples.meters import AverageMeter
  from examples.util import NoopContextManager
  from torchvision import datasets, transforms
label_desc: Use image node_id 7 for calling a global function with example usage: validate(val_loader, model, criterion, print_freq) and returns: top1