class_docstr: DatasetBuilder for quality dataset.
class_docstr_tok: ["DatasetBuilder","for","quality","dataset","."]
docstr: Returns the dataset metadata.
docstr_tok: ["Returns","the","dataset","metadata","."]
code: def _info(self) -> tfds.core.DatasetInfo:
      """Returns the dataset metadata."""
      # Mirror format of RACE dataset as much as possible.
      features_dict = {
          "article": tfds.features.Text(),
          # The sequence lengths of Sequence fields should match.
          "questions": tfds.features.Sequence(tfds.features.Text()),
          "question_ids": tfds.features.Sequence(tfds.features.Text()),
          # 4 options per question, similar to RACE
          "options": tfds.features.Sequence(
              tfds.features.Sequence(tfds.features.Text())
          ),
          "gold_labels": tfds.features.Sequence(np.int32),  # 0, 1, 2, 3
          "writer_labels": tfds.features.Sequence(
              np.int32
          ),  # 0, 1, 2, 3
          "difficults": tfds.features.Sequence(np.bool_),
      }
      features_dict.update(
          {k: tfds.features.Text() for k in _ONE2ONE_FIELDS}
      )
      return self.dataset_info_from_configs(
          # Note: some fields are left out.
          features=tfds.features.FeaturesDict(features_dict),
          supervised_keys=None,
          homepage="https://github.com/nyu-mll/quality",
      )
  
file_path: datasets/tensorflow_datasets/datasets/quality/quality_dataset_builder.py
imports: import dataclasses
  import json
  import numpy
  import tensorflow_datasets.public_api
label_desc: Use image node_id 1 for calling the Builder obj's underlying member method code with example usage: obj._info() and returns: self