class_docstr: FER 2013 Dataset
  homepage="https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge"
class_docstr_tok: ["FER","2013","Dataset","homepage=","''","https",":","\/\/www.kaggle.com\/c\/challenges-in-representation-learning-facial-expression-recognition-challenge","''"]
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"}
      )
      self._categories = _info()["categories"]
  
      super().__init__(root, skip_integrity_check=skip_integrity_check)
  
file_path: vision/torchvision/prototype/datasets/_builtin/fer2013.py
imports: import pathlib
  from typing import Any, Dict, List, Union
  import torch
  from torchdata.datapipes.iter import CSVDictParser, IterDataPipe, Mapper
  from torchvision.prototype.datasets.utils import Dataset, KaggleDownloadResource, OnlineResource
  from torchvision.prototype.datasets.utils._internal import hint_sharding, hint_shuffling
  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 FER2013 object from inherited base classes: Dataset with example: obj = FER2013(root)