class_docstr: BuilderConfig for UnifiedQA.
docstr: BuilderConfig for UnifiedQA.
  
  Args:
    features: `tfds.features.FeaturesDict`, specific feature dict for the
      dataset.
    data_urls: `dict`, urls to download the files from
    citation: `string`, citation for the data set
    header: `bool`, whether the data files have a header
    **kwargs: keyword arguments forwarded to super.
docstr_tok: ["BuilderConfig","for","UnifiedQA",".","Args",":","features",":","`","tfds.features.FeaturesDict","`",",","specific","feature","dict","for","the","dataset",".","data_urls",":","`","dict","`",",","urls","to","download","the","files","from","citation",":","`","string","`",",","citation","for","the","data","set","header",":","`","bool","`",",","whether","the","data","files","have","a","header","*","*","kwargs",":","keyword","arguments","forwarded","to","super","."]
code: def __init__(
      self, *, features, data_urls, citation, header=False, **kwargs
  ):
      """BuilderConfig for UnifiedQA.
  
      Args:
        features: `tfds.features.FeaturesDict`, specific feature dict for the
          dataset.
        data_urls: `dict`, urls to download the files from
        citation: `string`, citation for the data set
        header: `bool`, whether the data files have a header
        **kwargs: keyword arguments forwarded to super.
      """
      super(UnifiedQAConfig, self).__init__(**kwargs)
      self.features = features
      self.data_urls = data_urls
      self.citation = citation
      self.header = header
  
file_path: datasets/tensorflow_datasets/text/unifiedqa/unifiedqa.py
imports: from __future__ import annotations
  import csv
  import textwrap
  from etils import epath
  import numpy
  import tensorflow_datasets.public_api
label_desc: Use image node_id 1 to create a new UnifiedQAConfig object from inherited base classes: tfds.core with example: obj = UnifiedQAConfig()