def METHOD_NAME(model, dataloader_test):
  top1 = AverageMeter('Acc@1', ':6.2f')
  model.eval()
  with torch.no_grad():
    for i, (images, targets) in enumerate(dataloader_test):
      images = images.cuda()
      targets = targets.cuda()
      outputs = model(images)
      acc1, _ = accuracy(outputs, targets, topk=(1, 5))
      top1.update(acc1[0], images.size(0))
  return float(top1.avg)