code: def get_cifar(cfg):
      """Gets training and validation data loaders for the CIFAR datasets
  
      Args:
          cfg (CfgNode): hyperparameters from configure file
  
      Examples:
          >>> train_loader, valid_loader = get_cifar(cfg)
      """
      logging.info(
          "==> Preparing to load data "
          + cfg.DATASET.NAME
          + " at "
          + cfg.DATASET.ROOT
      )
      cifar_train_transform = get_transform("cifar", augment=True)
      cifar_test_transform = get_transform("cifar", augment=False)
  
      if cfg.DATASET.NAME == "CIFAR10":
          train_set = datasets.CIFAR10(
              cfg.DATASET.ROOT,
              train=True,
              download=cfg.DATASET.DOWNLOAD,
              transform=cifar_train_transform,
          )
          valid_set = datasets.CIFAR10(
              cfg.DATASET.ROOT,
              train=False,
              download=cfg.DATASET.DOWNLOAD,
              transform=cifar_test_transform,
          )
      elif cfg.DATASET.NAME == "CIFAR100":
          train_set = datasets.CIFAR100(
              cfg.DATASET.ROOT,
              train=True,
              download=cfg.DATASET.DOWNLOAD,
              transform=cifar_train_transform,
          )
          valid_set = datasets.CIFAR100(
              cfg.DATASET.ROOT,
              train=False,
              download=cfg.DATASET.DOWNLOAD,
              transform=cifar_test_transform,
          )
      else:
          raise NotImplementedError
  
      train_loader = torch.utils.data.DataLoader(
          train_set,
          batch_size=cfg.SOLVER.TRAIN_BATCH_SIZE,
          shuffle=True,
          num_workers=cfg.DATASET.NUM_WORKERS,
          pin_memory=True,
          drop_last=True,
      )
      valid_loader = torch.utils.data.DataLoader(
          valid_set,
          batch_size=cfg.SOLVER.TEST_BATCH_SIZE,
          shuffle=False,
          num_workers=cfg.DATASET.NUM_WORKERS,
          pin_memory=True,
      )
  
      return train_loader, valid_loader
  
imports: import glob
  import logging
  import os
  from enum import Enum
  import numpy
  import pydicom
  import torch
  from torchvision import datasets, transforms
  from kale.loaddata.dataset_access import DatasetAccess
  from kale.loaddata.mnistm import MNISTM
  from kale.loaddata.multi_domain import MultiDomainAccess, MultiDomainImageFolder
  from kale.loaddata.usps import USPS
  from kale.prepdata.image_transform import get_transform
  from kale.utils.download import download_file_by_url
label_desc: Use image node_id 1 for calling a global function with example usage: get_cifar(cfg) and returns: train_loader,  valid_loader