update docstrings

from transformers import RobertaTokenizerFast

from deepchem.utils.typing import RDKitMol

from typing import Dict, List

class RobertaFeaturizer(RobertaTokenizerFast, MolecularFeaturizer):

    super().__init__(**kwargs)

    return

  def _featurize(self, mol: RDKitMol, **kwargs):

  def _featurize(self, mol: RDKitMol, **kwargs) -> List[List[int]]:

    """Calculate encoding using HuggingFace's RobertaTokenizerFast

        Parameters

        Returns

        -------

        np.ndarray

          1D array of RDKit descriptors for `mol`. The length is 881.

        encoding: List

          List containing two lists; the `input_ids` and the `attention_mask`

        """

    try:

    encoding = list(self(smiles_string, **kwargs).values())

    return encoding

  def __call__(self, *args, **kwargs):

  def __call__(self, *args, **kwargs) -> Dict[str, List[int]]:

    return super().__call__(*args, **kwargs)