code: def _get_imagenet_tiny(args):
      traindir = os.path.join(args.data, "train")
      validdir = os.path.join(args.data, "val")
      normalize = transforms.Normalize(
          mean=[0.4802, 0.4481, 0.3975], std=[0.2302, 0.2265, 0.2262]
      )
      train_transform = transforms.Compose(
          [
              transforms.RandomCrop(64, padding=4),
              transforms.RandomHorizontalFlip(),
              transforms.ToTensor(),
              normalize,
          ]
      )
      if args.cutout:
          train_transform.transforms.append(Cutout(args.cutout_length))
  
      train_data = dset.ImageFolder(traindir, train_transform)
      valid_data = dset.ImageFolder(
          validdir,
          transforms.Compose(
              [
                  transforms.ToTensor(),
                  normalize,
              ]
          ),
      )
  
      train_queue = torch.utils.data.DataLoader(
          train_data,
          batch_size=args.batch_size,
          shuffle=True,
          pin_memory=True,
          num_workers=4,
      )
  
      valid_queue = torch.utils.data.DataLoader(
          valid_data,
          batch_size=args.batch_size // 2,
          shuffle=False,
          pin_memory=True,
          num_workers=4,
      )
      return train_queue, valid_queue
  
imports: import math
  import os
  import random
  import sys
  import time
  import warnings
  import numpy
  import numpy
  import shutil
  import logging
  import torch
  import torchvision.transforms
  import torchvision.datasets
  import torch.nn
label_desc: Use image node_id 10 for calling a global function with example usage: _get_imagenet_tiny(args) and returns: train_queue,  valid_queue