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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def add_distances(self, indices, periodic=True, indices2=None):
 r""" Adds the distances between atoms to the feature list. Parameters indices : can be of two types: ndarray((n, 2), dtype=int):
 n x 2 array with the pairs of atoms between which the distances shall be computed iterable of integers (either list or ndarray(n, dtype=int)):
 indices (not pairs of indices) of the atoms between which the distances shall be computed. periodic : optional, boolean, default is True If periodic is True and the trajectory contains unitcell information, distances will be computed under the minimum image convention. indices2: iterable of integers (either list or ndarray(n, dtype=int)), optional: Only has effect if :py:obj:`indices` is an iterable of integers. Instead of the above behaviour, only the distances between the atoms in :py:obj:`indices` and :py:obj:`indices2` will be computed. .. note:: When using the iterable of integers input, :py:obj:`indices` and :py:obj:`indices2` will be sorted numerically and made unique before converting them to a pairlist. Please look carefully at the output of :py:func:`describe()` to see what features exactly have been added. """

        from .distances import DistanceFeature

        atom_pairs = _parse_pairwise_input(
            indices, indices2, self.logger, fname='add_distances()')

        atom_pairs = self._check_indices(atom_pairs)
        f = DistanceFeature(self.topology, atom_pairs, periodic=periodic)
        self.__add_feature(f)