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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 kron_reduction(G, ind):
 r"""Compute the Kron reduction. This function perform the Kron reduction of the weight matrix in the graph *G*, with boundary nodes labeled by *ind*. This function will create a new graph with a weight matrix Wnew that contain only boundary nodes and is computed as the Schur complement of the original matrix with respect to the selected indices. Parameters G : Graph or sparse matrix Graph structure or weight matrix ind : list indices of the nodes to keep Returns ------- Gnew : Graph or sparse matrix New graph structure or weight matrix References See :cite:`dorfler2013kron` """

    if isinstance(G, graphs.Graph):

        if G.lap_type != 'combinatorial':
                msg = 'Unknown reduction for {} Laplacian.'.format(G.lap_type)
                raise NotImplementedError(msg)

        if G.is_directed():
            msg = 'This method only work for undirected graphs.'
            raise NotImplementedError(msg)

        L = G.L

    else:

        L = G

    N = np.shape(L)[0]
    ind_comp = np.setdiff1d(np.arange(N, dtype=int), ind)

    L_red = L[np.ix_(ind, ind)]
    L_in_out = L[np.ix_(ind, ind_comp)]
    L_out_in = L[np.ix_(ind_comp, ind)].tocsc()
    L_comp = L[np.ix_(ind_comp, ind_comp)].tocsc()

    Lnew = L_red - L_in_out.dot(linalg.spsolve(L_comp, L_out_in))

    # Make the laplacian symmetric if it is almost symmetric!
    if np.abs(Lnew - Lnew.T).sum() < np.spacing(1) * np.abs(Lnew).sum():
        Lnew = (Lnew + Lnew.T) / 2.

    if isinstance(G, graphs.Graph):
        # Suppress the diagonal ? This is a good question?
        Wnew = sparse.diags(Lnew.diagonal(), 0) - Lnew
        Snew = Lnew.diagonal() - np.ravel(Wnew.sum(0))
        if np.linalg.norm(Snew, 2) >= np.spacing(1000):
            Wnew = Wnew + sparse.diags(Snew, 0)

        # Removing diagonal for stability
        Wnew = Wnew - Wnew.diagonal()

        coords = G.coords[ind, :] if len(G.coords.shape) else np.ndarray(None)
        Gnew = graphs.Graph(Wnew, coords=coords, lap_type=G.lap_type,
                            plotting=G.plotting)
    else:
        Gnew = Lnew

    return Gnew