class_docstr: DatasetBuilder for mslr_web dataset.
class_docstr_tok: ["DatasetBuilder","for","mslr_web","dataset","."]
docstr: Returns the dataset metadata.
docstr_tok: ["Returns","the","dataset","metadata","."]
code: def _info(self) -> tfds.core.DatasetInfo:
      """Returns the dataset metadata."""
      encoding = tfds.features.Encoding.ZLIB
      features = {
          "float_features": tfds.features.Tensor(
              shape=(None, len(_FEATURE_NAMES)),
              dtype=np.float64,
              encoding=encoding,
          ),
          _LABEL_NAME: tfds.features.Tensor(
              shape=(None,), dtype=np.float64, encoding=encoding
          ),
          "query_id": tfds.features.Text(),
          "doc_id": tfds.features.Tensor(
              shape=(None,), dtype=np.int64, encoding=encoding
          ),
      }
      metadata = tfds.core.MetadataDict(
          {
              "float_features_names": {
                  idx: _FEATURE_NAMES[key]
                  for idx, key in enumerate(sorted(_FEATURE_NAMES))
              }
          }
      )
  
      return tfds.core.DatasetInfo(
          builder=self,
          description=_DESCRIPTION,
          features=tfds.features.FeaturesDict(features),
          homepage="https://www.microsoft.com/en-us/research/project/mslr/",
          citation=_CITATION,
          metadata=metadata,
      )
  
file_path: datasets/tensorflow_datasets/ranking/mslr_web/mslr_web.py
imports: import dataclasses
  import itertools
  import numpy
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
  from tensorflow_datasets.ranking.libsvm_ranking_parser import LibSVMRankingParser
label_desc: Use image node_id 1 for calling the MslrWeb obj's underlying member method code with example usage: obj._info() and returns: tfds