class_docstr: Machine translations of the MNLI dataset.
code: def _info(self):
      return tfds.core.DatasetInfo(
          builder=self,
          description=_DESCRIPTION,
          features=tfds.features.FeaturesDict(
              {
                  "premise": tfds.features.Translation(
                      languages=_LANGUAGES,
                  ),
                  "hypothesis": tfds.features.TranslationVariableLanguages(
                      languages=_LANGUAGES,
                  ),
                  "label": tfds.features.ClassLabel(
                      names=["entailment", "neutral", "contradiction"]
                  ),
              }
          ),
          # No default supervised_keys (as we have to pass both premise
          # and hypothesis as input).
          supervised_keys=None,
          homepage="https://www.nyu.edu/projects/bowman/xnli/",
          citation=_CITATION,
      )
  
file_path: datasets/tensorflow_datasets/text/xtreme_xnli/xtreme_xnli.py
imports: import collections
  import csv
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
label_desc: Use image node_id 1 for calling the XtremeXnli obj's underlying member method code with example usage: obj._info() and returns: tfds