code: def __init__(
      self,
      name: str,
      *,
      variant=None,
      legacy_cls=None,
      new_config=None,
      legacy_config_map=None,
      legacy_special_options_map=None,
      prepare_legacy_root=None,
  ):
      self.name = name
      self.variant = variant
  
      self.new_raw_dataset = new_datasets._api.find(name)
      self.legacy_cls = legacy_cls or self._find_legacy_cls()
  
      if new_config is None:
          new_config = self.new_raw_dataset.default_config
      elif isinstance(new_config, dict):
          new_config = self.new_raw_dataset.info.make_config(
              **new_config
          )
      self.new_config = new_config
  
      self.legacy_config_map = legacy_config_map
      self.legacy_special_options_map = (
          legacy_special_options_map or self._legacy_special_options_map
      )
      self.prepare_legacy_root = prepare_legacy_root
  
imports: import argparse
  import collections.abc
  import contextlib
  import inspect
  import itertools
  import os
  import os.path
  import pathlib
  import shutil
  import sys
  import tempfile
  import time
  import unittest.mock
  import warnings
  import torch
  from torch.utils.data import DataLoader
  from torch.utils.data.dataloader_experimental import DataLoader2
  from torchvision import datasets
  from torchvision.datasets.utils import extract_archive
  from torchvision.prototype import datasets
  from torchvision.transforms import PILToTensor
label_desc: Use image node_id 1 to create a new DatasetBenchmark object  with example: obj = DatasetBenchmark(name)