def METHOD_NAME(self):
    """
    Compute the indices into the data vector corresponding to non-distributed variables.
    Returns
    -------
    ndarray of int
        Index array corresponding to non-distributed variables.
    """
    if self._dup_inds is None:
        system = self._system()
        if system.comm.size > 1:
            # Here, we find the indices that are not locally owned so that we can
            # temporarilly zero them out for the norm calculation.
            dup_inds = []
            abs2meta = system._var_allprocs_abs2meta[self._typ]
            for name, idx_slice in self.get_slice_dict().items():
                owning_rank = system._owning_rank[name]
                if not abs2meta[name]['distributed'] and owning_rank != system.comm.rank:
                    dup_inds.extend(range(idx_slice.start, idx_slice.stop))
            self._dup_inds = np.array(dup_inds, dtype=INT_DTYPE)
            if len(dup_inds) > 0:
                self._dup_scratch = np.empty(idx_slice.stop)
        else:
            self._dup_inds = np.array([], dtype=INT_DTYPE)
    return self._dup_inds