Add returns and examples to docstring

    Path to featurized datasets.

  **kwargs: optional arguments to featurizers and splitters.

  Returns

  -------

  tasks, datasets, transformers : iterable

    tasks : list

      Column names corresponding to machine learning target variables.

    datasets : tuple

      train, validation, test splits of data as

      ``deepchem.data.datasets.Dataset`` instances.

    transformers : list

      ``deepchem.trans.transformers.Transformer`` instances applied

      to dataset.

  References

  ----------

  MLA style references for this dataset. E.g.

    Last, First et al. "Article title." Journal name, vol. #,

      no. #, year, pp. page range, DOI. 

  Examples

  --------

  >>> import deepchem as dc

  >>> tasks, datasets, transformers = dc.molnet.load_mydataset()

  >>> train_dataset, val_dataset, test_dataset = datasets

  >>> n_tasks = len(tasks)

  >>> n_features = train_dataset.get_data_shape()[0]

  >>> model = dc.models.MultiTaskClassifier(n_tasks, n_features)

  """

  # Featurize mydataset