code: def mask(x, indices):
      
      indices_shape = (
          [len(indices)] if isinstance(indices, list) else indices.shape)
      if not indices_shape[0]:
          return np.array([])
      elif is_sparse_vector(x) and len(indices_shape) == 1:
          return x[0, indices].toarray()[0]
      else:
          return x[indices]
docs: The same as x[indices], but return an empty array if indices are empty,
      instead of returning all x elements,
      and handles sparse "vectors".