How to use the pypsa.linopt.write_constraint function in pypsa

glcs = n.global_constraints.query('type == '
                                      '"transmission_volume_expansion_limit"')
    substr = lambda s: re.sub('[\[\]\(\)]', '', s)
    for name, glc in glcs.iterrows():
        car = [substr(c.strip()) for c in glc.carrier_attribute.split(',')]
        lhs = ''
        for c, attr in (('Line', 's_nom'), ('Link', 'p_nom')):
            ext_i = n.df(c).query(f'carrier in @car and {attr}_extendable').index
            if ext_i.empty: continue
            v = linexpr((n.df(c).length[ext_i], get_var(n, c, attr)[ext_i]),
                        as_pandas=False)
            lhs += '\n' + join_exprs(v)
        if lhs == '': continue
        sense = glc.sense
        rhs = glc.constant
        con = write_constraint(n, lhs, sense, rhs, axes=pd.Index([name]))
        set_conref(n, con, 'GlobalConstraint', 'mu', name)

    # expansion cost limits
    glcs = n.global_constraints.query('type == '
                                      '"transmission_expansion_cost_limit"')
    for name, glc in glcs.iterrows():
        car = [substr(c.strip()) for c in glc.carrier_attribute.split(',')]
        lhs = ''
        for c, attr in (('Line', 's_nom'), ('Link', 'p_nom')):
            ext_i = n.df(c).query(f'carrier in @car and {attr}_extendable').index
            if ext_i.empty: continue
            v = linexpr((n.df(c).capital_cost[ext_i], get_var(n, c, attr)[ext_i]),
                        as_pandas=False)
            lhs += '\n' + join_exprs(v)
        if lhs == '': continue
        sense = glc.sense

'state_of_charge').loc[sns[-1], sus_i])
            vals = linexpr(coeff_val, as_pandas=False)
            lhs = lhs + '\n' + join_exprs(vals)
            rhs -= sus.carrier.map(emissions) @ sus.state_of_charge_initial

        # stores
        n.stores['carrier'] = n.stores.bus.map(n.buses.carrier)
        stores = n.stores.query('carrier in @emissions.index and not e_cyclic')
        if not stores.empty:
            coeff_val = (-stores.carrier.map(emissions), get_var(n, 'Store', 'e')
                         .loc[sns[-1], stores.index])
            vals = linexpr(coeff_val, as_pandas=False)
            lhs = lhs + '\n' + join_exprs(vals)
            rhs -= stores.carrier.map(emissions) @ stores.e_initial

        con = write_constraint(n, lhs, glc.sense, rhs, axes=pd.Index([name]))
        set_conref(n, con, 'GlobalConstraint', 'mu', name)

    # for the next two to we need a line carrier
    if len(n.global_constraints) > len(glcs):
        n.lines['carrier'] = n.lines.bus0.map(n.buses.carrier)
    # expansion limits
    glcs = n.global_constraints.query('type == '
                                      '"transmission_volume_expansion_limit"')
    substr = lambda s: re.sub('[\[\]\(\)]', '', s)
    for name, glc in glcs.iterrows():
        car = [substr(c.strip()) for c in glc.carrier_attribute.split(',')]
        lhs = ''
        for c, attr in (('Line', 's_nom'), ('Link', 'p_nom')):
            ext_i = n.df(c).query(f'carrier in @car and {attr}_extendable').index
            if ext_i.empty: continue
            v = linexpr((n.df(c).length[ext_i], get_var(n, c, attr)[ext_i]),

ds = ds[lambda ds: ds!=0.].dropna()
        vals = linexpr((ds, branch_vars[ds.index]), as_pandas=False)
        return vals.sum(1)

    constraints = []
    for sub in n.sub_networks.obj:
        branches = sub.branches()
        C = pd.DataFrame(sub.C.todense(), index=branches.index)
        if C.empty:
            continue
        carrier = n.sub_networks.carrier[sub.name]
        weightings = branches.x_pu_eff if carrier == 'AC' else branches.r_pu_eff
        C_weighted = 1e5 * C.mul(weightings, axis=0)
        cycle_sum = C_weighted.apply(cycle_flow)
        cycle_sum.index = sns
        con = write_constraint(n, cycle_sum, '=', 0)
        constraints.append(con)
    constraints = pd.concat(constraints, axis=1, ignore_index=True)
    set_conref(n, constraints, 'SubNetwork', 'mu_kirchhoff_voltage_law')

n : pypsa.Network
    c : str
        name of the network component
    attr : str
        name of the attribute, e.g. 'p'
    pnl : bool, default True
        Whether variable which should be fixed is time-dependent

    """

    if pnl:
        if attr + '_set' not in n.pnl(c): return
        fix = n.pnl(c)[attr + '_set'].unstack().dropna()
        if fix.empty: return
        lhs = linexpr((1, get_var(n, c, attr).unstack()[fix.index]), as_pandas=False)
        constraints = write_constraint(n, lhs, '=', fix).unstack().T
    else:
        if attr + '_set' not in n.df(c): return
        fix = n.df(c)[attr + '_set'].dropna()
        if fix.empty: return
        lhs = linexpr((1, get_var(n, c, attr)[fix.index]), as_pandas=False)
        constraints = write_constraint(n, lhs, '=', fix)
    set_conref(n, constraints, c, f'mu_{attr}_set')