code: def get_dataset(args):
      if torch.cuda.is_available() and not args.cuda:
          print(
              "WARNING: You have a CUDA device, so you should probably run with --cuda"
          )
      if args.dataroot is None and str(args.dataset).lower() != "fake":
          raise ValueError(
              '`dataroot` parameter is required for dataset "%s"'
              % args.dataset
          )
      if args.dataset in ["imagenet", "folder", "lfw"]:
          # folder dataset
          dataset = dset.ImageFolder(
              root=args.dataroot,
              transform=transforms.Compose(
                  [
                      transforms.Resize(args.imageSize),
                      transforms.CenterCrop(args.imageSize),
                      transforms.ToTensor(),
                      transforms.Normalize(
                          (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)
                      ),
                  ]
              ),
          )
          nc = 3
      elif args.dataset == "lsun":
          classes = [c + "_train" for c in args.classes.split(",")]
          dataset = dset.LSUN(
              root=args.dataroot,
              classes=classes,
              transform=transforms.Compose(
                  [
                      transforms.Resize(args.imageSize),
                      transforms.CenterCrop(args.imageSize),
                      transforms.ToTensor(),
                      transforms.Normalize(
                          (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)
                      ),
                  ]
              ),
          )
          nc = 3
      elif args.dataset == "cifar10":
          dataset = dset.CIFAR10(
              root=args.dataroot,
              download=True,
              transform=transforms.Compose(
                  [
                      transforms.Resize(args.imageSize),
                      transforms.ToTensor(),
                      transforms.Normalize(
                          (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)
                      ),
                  ]
              ),
          )
          nc = 3
      elif args.dataset == "mnist":
          dataset = dset.MNIST(
              root=args.dataroot,
              download=True,
              transform=transforms.Compose(
                  [
                      transforms.Resize(args.imageSize),
                      transforms.ToTensor(),
                      transforms.Normalize((0.5,), (0.5,)),
                  ]
              ),
          )
          nc = 1
  
      elif args.dataset == "fake":
          dataset = dset.FakeData(
              image_size=(3, args.imageSize, args.imageSize),
              transform=transforms.ToTensor(),
          )
          nc = 3
  
      elif args.dataset == "celeba":
          dataset = dset.ImageFolder(
              root=args.dataroot,
              transform=transforms.Compose(
                  [
                      transforms.Resize(args.imageSize),
                      transforms.CenterCrop(args.imageSize),
                      transforms.ToTensor(),
                      transforms.Normalize(
                          (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)
                      ),
                  ]
              ),
          )
          nc = 3
  
      assert dataset
      return dataset, nc
  
file_path: deepspeedexamples/training/gan/gan_baseline_train.py
imports: import torch
  import torch.backends.cudnn
  import torch.nn
  import torch.utils.data
  import torchvision.datasets
  import torchvision.transforms
  import torchvision.utils
  from torch.utils.tensorboard import SummaryWriter
  from time import time
  from gan_model import Generator, Discriminator, weights_init
  from utils import get_argument_parser, set_seed, create_folder
label_desc: Use image node_id 1 for calling a global function with example usage: get_dataset(args) and returns: dataset,  nc