code: def validate_shift(val_loader, model, args):
      batch_time = AverageMeter()
      consist = AverageMeter()
  
      # switch to evaluate mode
      model.eval()
  
      with torch.no_grad():
          end = time.time()
          for ep in range(args.epochs_shift):
              for i, (input, target) in enumerate(val_loader):
                  if args.gpu is not None:
                      input = input.cuda(args.gpu, non_blocking=True)
                  target = target.cuda(args.gpu, non_blocking=True)
  
                  off0 = np.random.randint(32, size=2)
                  off1 = np.random.randint(32, size=2)
  
                  output0 = model(
                      input[
                          :,
                          :,
                          off0[0] : off0[0] + 224,
                          off0[1] : off0[1] + 224,
                      ]
                  )
                  output1 = model(
                      input[
                          :,
                          :,
                          off1[0] : off1[0] + 224,
                          off1[1] : off1[1] + 224,
                      ]
                  )
  
                  cur_agree = (
                      agreement(output0, output1)
                      .type(torch.FloatTensor)
                      .to(output0.device)
                  )
  
                  # measure agreement and record
                  consist.update(cur_agree.item(), input.size(0))
  
                  # measure elapsed time
                  batch_time.update(time.time() - end)
                  end = time.time()
  
                  if i % args.print_freq == 0:
                      print(
                          "Ep [{0}/{1}]:\t"
                          "Test: [{2}/{3}]\t"
                          "Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t"
                          "Consist {consist.val:.4f} ({consist.avg:.4f})\t".format(
                              ep,
                              args.epochs_shift,
                              i,
                              len(val_loader),
                              batch_time=batch_time,
                              consist=consist,
                          )
                      )
  
          print(
              " * Consistency {consist.avg:.3f}".format(consist=consist)
          )
  
      return consist.avg
  
imports: import argparse
  import os
  import random
  import shutil
  import time
  import warnings
  import sys
  import numpy
  import os
  import torch
  import torch.nn
  import torch.nn.parallel
  import torch.backends.cudnn
  import torch.distributed
  import torch.optim
  import torch.multiprocessing
  import torch.utils.data
  import torch.utils.data.distributed
  import torchvision.transforms
  import torchvision.datasets
  import antialiased_cnns
  import torchvision.models
label_desc: Use image node_id 5 for calling a global function with example usage: validate_shift(val_loader, model, args) and returns: consist