code: def check_dataset(dataset, dataroot):
      """
  
      Args:
          dataset (str): Name of the dataset to use. See CLI help for details
          dataroot (str): root directory where the dataset will be stored.
  
      Returns:
          dataset (data.Dataset): torchvision Dataset object
  
      """
      resize = transforms.Resize(64)
      crop = transforms.CenterCrop(64)
      to_tensor = transforms.ToTensor()
      normalize = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
  
      if dataset in {"imagenet", "folder", "lfw"}:
          dataset = dset.ImageFolder(
              root=dataroot,
              transform=transforms.Compose(
                  [resize, crop, to_tensor, normalize]
              ),
          )
          nc = 3
  
      elif dataset == "lsun":
          dataset = dset.LSUN(
              root=dataroot,
              classes=["bedroom_train"],
              transform=transforms.Compose(
                  [resize, crop, to_tensor, normalize]
              ),
          )
          nc = 3
  
      elif dataset == "cifar10":
          dataset = dset.CIFAR10(
              root=dataroot,
              download=True,
              transform=transforms.Compose(
                  [resize, to_tensor, normalize]
              ),
          )
          nc = 3
  
      elif dataset == "mnist":
          dataset = dset.MNIST(
              root=dataroot,
              download=True,
              transform=transforms.Compose(
                  [resize, to_tensor, normalize]
              ),
          )
          nc = 1
  
      elif dataset == "fake":
          dataset = dset.FakeData(
              size=256, image_size=(3, 64, 64), transform=to_tensor
          )
          nc = 3
  
      else:
          raise RuntimeError(f"Invalid dataset name: {dataset}")
  
      return dataset, nc
  
imports: import argparse
  import os
  import random
  import warnings
  from pathlib import Path
  import torch
  import torch.nn
  import torch.optim
  import torch.utils.data
  from ignite.contrib.handlers import ProgressBar
  from ignite.engine import Engine, Events
  from ignite.handlers import ModelCheckpoint, Timer
  from ignite.metrics import RunningAverage
label_desc: Use image node_id 2 for calling a global function with example usage: check_dataset(dataset, dataroot) and returns: dataset,  nc