code: def getData(name="cifar10", train_bs=128, test_bs=1000):
      """
      Get the dataloader
      """
      if name == "cifar10":
          transform_train = transforms.Compose(
              [
                  transforms.RandomCrop(32, padding=4),
                  transforms.RandomHorizontalFlip(),
                  transforms.ToTensor(),
                  transforms.Normalize(
                      (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)
                  ),
              ]
          )
  
          transform_test = transforms.Compose(
              [
                  transforms.ToTensor(),
                  transforms.Normalize(
                      (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)
                  ),
              ]
          )
  
          trainset = datasets.CIFAR10(
              root="../data",
              train=True,
              download=True,
              transform=transform_train,
          )
          train_loader = torch.utils.data.DataLoader(
              trainset, batch_size=train_bs, shuffle=True
          )
  
          testset = datasets.CIFAR10(
              root="../data",
              train=False,
              download=False,
              transform=transform_test,
          )
          test_loader = torch.utils.data.DataLoader(
              testset, batch_size=test_bs, shuffle=False
          )
      return train_loader, test_loader
  
file_path: deepspeedexamples/compression/cifar/utils.py
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torchvision import datasets, transforms
  from torch.autograd import Variable
label_desc: Use image node_id 1 for calling a global function with example usage: getData(name, train_bs, test_bs) and returns: train_loader,  test_loader