code: def fetch_lawschool_gpa(
      subset="all",
      *,
      usecols=None,
      dropcols=None,
      numeric_only=False,
      dropna=True,
  ):
      """Load the Law School GPA dataset
  
      Note:
          By default, the data is downloaded from tempeh. See
          https://github.com/microsoft/tempeh for details.
  
      Args:
          subset ({'train', 'test', or 'all'}, optional): Select the dataset to
              load: 'train' for the training set, 'test' for the test set, 'all'
              for both.
          usecols (single label or list-like, optional): Feature column(s) to
              keep. All others are dropped.
          dropcols (single label or list-like, optional): Feature column(s) to
              drop.
          numeric_only (bool): Drop all non-numeric feature columns.
          dropna (bool): Drop rows with NAs. FIXME: NAs already dropped by tempeh
  
      Returns:
          namedtuple: Tuple containing X, y, and sample_weights for the Law School
          GPA dataset accessible by index or name.
      """
      if subset not in {"train", "test", "all"}:
          raise ValueError(
              "subset must be either 'train', 'test', or 'all'; "
              "cannot be {}".format(subset)
          )
  
      dataset = tc.datasets["lawschool_gpa"]()
      X_train, X_test = dataset.get_X(format=pd.DataFrame)
      y_train, y_test = dataset.get_y(format=pd.Series)
      A_train, A_test = dataset.get_sensitive_features(
          name="race", format=pd.Series
      )
      all_train = pd.concat([X_train, y_train, A_train], axis=1)
      all_test = pd.concat([X_test, y_test, A_test], axis=1)
  
      if subset == "train":
          df = all_train
      elif subset == "test":
          df = all_test
      else:
          df = pd.concat([all_train, all_test], axis=0)
  
      df.race = df.race.astype("category").cat.set_categories(
          ["black", "white"], ordered=True
      )
  
      return standardize_dataset(
          df,
          prot_attr="race",
          target="zfygpa",
          usecols=usecols,
          dropcols=dropcols,
          numeric_only=numeric_only,
          dropna=dropna,
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: fetch_lawschool_gpa(subset) and returns: standardize_dataset