class_docstr: SVHN Dataset.
  homepage="http://ufldl.stanford.edu/housenumbers/",
  dependencies = scipy
class_docstr_tok: ["SVHN","Dataset",".","homepage=","''","http",":","\/\/ufldl.stanford.edu\/housenumbers\/","''",",","dependencies","=","scipy"]
code: def __init__(
      self,
      root: Union[str, pathlib.Path],
      *,
      split: str = "train",
      skip_integrity_check: bool = False,
  ) -> None:
      self._split = self._verify_str_arg(
          split, "split", {"train", "test", "extra"}
      )
      self._categories = _info()["categories"]
      super().__init__(
          root,
          skip_integrity_check=skip_integrity_check,
          dependencies=("scipy",),
      )
  
file_path: vision/torchvision/prototype/datasets/_builtin/svhn.py
imports: import pathlib
  from typing import Any, BinaryIO, Dict, List, Tuple, Union
  import numpy
  from torchdata.datapipes.iter import IterDataPipe, Mapper, UnBatcher
  from torchvision.prototype.datasets.utils import Dataset, HttpResource, OnlineResource
  from torchvision.prototype.datasets.utils._internal import hint_sharding, hint_shuffling, read_mat
  from torchvision.prototype.tv_tensors import Label
  from torchvision.tv_tensors import Image
  from .._api import register_dataset, register_info
label_desc: Use image node_id 1 to create a new SVHN object from inherited base classes: Dataset with example: obj = SVHN(root)