code: def validate(self, is_test=True):
      if is_test:
          data_loader = self.test_loader
      else:
          data_loader = self.valid_loader
      self.model.eval()
      batch_time = AverageMeter("batch_time")
      losses = AverageMeter("losses")
      top1 = AverageMeter("top1")
      top5 = AverageMeter("top5")
  
      end = time.time()
      with torch.no_grad():
          for i, (images, labels) in enumerate(data_loader):
              images, labels = images.to(self.device), labels.to(
                  self.device
              )
              # compute output
              output = self.model(images)
              loss = self.criterion(output, labels)
              # measure accuracy and record loss
              acc1, acc5 = accuracy(output, labels, topk=(1, 5))
              losses.update(loss, 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 % 10 == 0 or i + 1 == len(data_loader):
                  if is_test:
                      prefix = "Test"
                  else:
                      prefix = "Valid"
                  test_log = (
                      prefix + ": [{0}/{1}]\t"
                      "Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t"
                      "Loss {loss.val:.4f} ({loss.avg:.4f})\t"
                      "Top-1 acc {top1.val:.3f} ({top1.avg:.3f})".format(
                          i,
                          len(data_loader) - 1,
                          batch_time=batch_time,
                          loss=losses,
                          top1=top1,
                      )
                  )
                  test_log += "\tTop-5 acc {top5.val:.3f} ({top5.avg:.3f})".format(
                      top5=top5
                  )
                  print(test_log)
      return losses.avg, top1.avg, top5.avg
  
file_path: nni/examples/nas/legacy/oneshot/proxylessnas/retrain.py
imports: import time
  import math
  from datetime import timedelta
  import torch
  from torch import nn
  from nni.nas.pytorch.utils import AverageMeter
label_desc: Use image node_id 5 for calling the Retrain obj's underlying member method code with example usage: obj.validate(is_test) and returns: losses,  top1,  top5