class_docstr: Adapter dataset to available datasets in Torchvision.
  The selected dataset is based on the name provided as argument.
  This name must match the names of the dataset in torchvision.
  
  Args:
      cfg (AttrDict): configuration defined by user
      data_source (string): data source ("torchvision_dataset") [not used]
      path (string): path to the dataset
      split (string): specify split for the dataset (either "train" or "val").
      dataset_name (string): name of dataset (should be one of TorchvisionDatasetName).
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code: def _load_dataset(self):
      is_train_split = self.split == "train"
      if self.dataset_name == TorchvisionDatasetName.CIFAR10:
          return CIFAR10(self.path, train=is_train_split)
      elif self.dataset_name == TorchvisionDatasetName.CIFAR100:
          return CIFAR100(self.path, train=is_train_split)
      elif self.dataset_name == TorchvisionDatasetName.STL10:
          stl_split = "train" if is_train_split else "test"
          return STL10(self.path, split=stl_split)
      elif self.dataset_name == TorchvisionDatasetName.MNIST:
          return MNIST(root=self.path, train=is_train_split)
      elif self.dataset_name == TorchvisionDatasetName.SVHN:
          stl_split = "train" if is_train_split else "test"
          return SVHN(root=self.path, split=stl_split)
      else:
          raise ValueError(f"Unsupported dataset {self.dataset_name}")
  
file_path: vissl/vissl/data/torchvision_dataset.py
imports: from typing import List, Tuple
  from iopath.common.file_io import g_pathmgr
  from PIL import Image
  from torch.utils.data import Dataset
  from torchvision.datasets import CIFAR10, CIFAR100, MNIST, STL10, SVHN
  from vissl.config import AttrDict
label_desc: Use image node_id 2 for calling the TorchvisionDataset obj's underlying member method code with example usage: obj._load_dataset() and returns: CIFAR10, CIFAR100, STL10, MNIST, SVHN