class_docstr: NaturalQuestions for Longt5.
class_docstr_tok: ["NaturalQuestions","for","Longt5","."]
docstr: BuilderConfig for Longt5.
  
  Args:
    citation: `string`, citation for the config.
    features: `dict[string, string]`, map from the name of the feature dict
      for each text field to the name of the column in the tsv file.
    **kwargs: keyword arguments forwarded to super.
docstr_tok: ["BuilderConfig","for","Longt5",".","Args",":","citation",":","`","string","`",",","citation","for","the","config",".","features",":","`","dict","[","string",",","string","]","`",",","map","from","the","name","of","the","feature","dict","for","each","text","field","to","the","name","of","the","column","in","the","tsv","file",".","*","*","kwargs",":","keyword","arguments","forwarded","to","super","."]
code: def __init__(
      self,
      *,
      citation: Optional[str] = None,
      features: Optional[tfds.features.FeaturesDict] = None,
      **kwargs,
  ):
      """BuilderConfig for Longt5.
  
      Args:
        citation: `string`, citation for the config.
        features: `dict[string, string]`, map from the name of the feature dict
          for each text field to the name of the column in the tsv file.
        **kwargs: keyword arguments forwarded to super.
      """
      super(NaturalQuestionsConfig, self).__init__(**kwargs)
      self.citation = citation
      self.features = features
  
file_path: datasets/tensorflow_datasets/datasets/natural_questions/natural_questions_dataset_builder.py
imports: import html
  import json
  import os
  import re
  from typing import Optional
  import numpy
  import tensorflow_datasets.public_api
label_desc: Use image node_id 1 to create a new NaturalQuestionsConfig object from inherited base classes: tfds.core with example: obj = NaturalQuestionsConfig()