code: """baseline_mr dataset."""
  
  import tensorflow_datasets as tfds
  import tensorflow as tf
  
  # TODO(baseline_mr): Markdown description  that will appear on the catalog page.
  _DESCRIPTION = """
  Description is **formatted** as markdown.
  
  It should also contain any processing which has been applied (if any),
  (e.g. corrupted example skipped, images cropped,...):
  """
  
  # TODO(baseline_mr): BibTeX citation
  _CITATION = """
  """
  
  
  class BaselineMr(tfds.core.GeneratorBasedBuilder):
    """DatasetBuilder for baseline_mr dataset."""
  
    VERSION = tfds.core.Version('1.0.0')
    RELEASE_NOTES = {
        '1.0.0': 'Initial release.',
    }
  
    def _info(self) -> tfds.core.DatasetInfo:
      """Returns the dataset metadata."""
      # TODO(baseline_mr): Specifies the tfds.core.DatasetInfo object
      return tfds.core.DatasetInfo(
          builder=self,
          description=_DESCRIPTION,
          features=tfds.features.FeaturesDict({
              'source': tfds.features.Text(),
              'target': tfds.features.Text(),
          }),
          homepage='https://dataset-homepage/',
          citation=_CITATION,
      )
  
    def _split_generators(self, dl_manager: tfds.download.DownloadManager):
      """Returns SplitGenerators."""
      # TODO(baseline_mr): Downloads the data and defines the splits
      path = dl_manager.download_and_extract('https://storage.googleapis.com/ai4b-anuvaad-nmt/baselines/mT5/baseline_mr/strict-en-mr.zip')
  
      # TODO(baseline_mr): Returns the Dict[split names, Iterator[Key, Example]]
      return {
          'train': self._generate_examples(source=path/'en-mr/train/train.mr', target=path/'en-mr/train/train.en'),
          'validation': self._generate_examples(source=path/'en-mr/dev/dev.mr', target=path/'en-mr/dev/dev.en')
      }
  
    def _generate_examples(self, source, target):
      """Yields examples."""
      # TODO(baseline_mr): Yields (key, example) tuples from the dataset
      src = tf.io.gfile.GFile(source, 'r').readlines()
      tgt = tf.io.gfile.GFile(target, 'r').readlines()
      for idx, row in enumerate(zip(src, tgt)):
        yield idx, {
            'source': row[0],
            'target': row[1],
        }
  
apis: tensorflow.io.gfile.GFile