class_docstr: BuilderConfig for MLQA.
class_docstr_tok: ["BuilderConfig","for","MLQA","."]
docstr: BuilderConfig for MLQA.
  
  Args:
    language: string, a valid language code.
    **kwargs: keyword arguments forwarded to super.
docstr_tok: ["BuilderConfig","for","MLQA",".","Args",":","language",":","string",",","a","valid","language","code",".","*","*","kwargs",":","keyword","arguments","forwarded","to","super","."]
code: def __init__(self, language, **kwargs):
      """BuilderConfig for MLQA.
  
      Args:
        language: string, a valid language code.
        **kwargs: keyword arguments forwarded to super.
      """
      super(MlqaConfig, self).__init__(
          version=tfds.core.Version("1.0.0"), **kwargs
      )
      self.language = language
  
file_path: datasets/tensorflow_datasets/datasets/mlqa/mlqa_dataset_builder.py
imports: import os
  import tensorflow_datasets.public_api
  from tensorflow_datasets.question_answering import qa_utils
label_desc: Use image node_id 1 to create a new MlqaConfig object from inherited base classes: tfds.core with example: obj = MlqaConfig(language)