How to use the pypsa.descriptors.nominal_attrs.items function in pypsa

def describe_lower_dispatch_constraints(n):
    description = {}
    key = ' Lower Limit'
    for c, attr in nominal_attrs.items():
        if c in ['Line', 'Transformer', 'Link']:
            dispatch_attr = 'p0'
            description[c] = pd.Series({'min':
                              (n.df(c)[attr + '_opt'] *
                              get_as_dense(n, c, attr[0] + '_max_pu') +
                              n.pnl(c)[dispatch_attr]).min().min()})
        else:
            dispatch_attr = attr[0]
            description[c + key] = pd.Series({'min':
                                   (-n.df(c)[attr + '_opt'] *
                                   get_as_dense(n, c, attr[0] + '_min_pu') +
                                   n.pnl(c)[dispatch_attr]).min().min()})
    return pd.concat(description, axis=1)

constant = 0
    for c, attr in nom_attr:
        ext_i = get_extendable_i(n, c)
        constant += n.df(c)[attr][ext_i] @ n.df(c).capital_cost[ext_i]
    object_const = write_bound(n, constant, constant)
    n.objective_f.write(linexpr((-1, object_const), as_pandas=False)[0])

    for c, attr in lookup.query('marginal_cost').index:
        cost = (get_as_dense(n, c, 'marginal_cost', sns)
                .loc[:, lambda ds: (ds != 0).all()]
                .mul(n.snapshot_weightings[sns], axis=0))
        if cost.empty: continue
        terms = linexpr((cost, get_var(n, c, attr).loc[sns, cost.columns]))
        n.objective_f.write(join_exprs(terms))
    # investment
    for c, attr in nominal_attrs.items():
        cost = n.df(c)['capital_cost'][get_extendable_i(n, c)]
        if cost.empty: continue
        terms = linexpr((cost, get_var(n, c, attr)[cost.index]))
        n.objective_f.write(join_exprs(terms))

def describe_upper_dispatch_constraints(n):
    '''
    Recalculates the minimum gap between operational status and nominal capacity
    '''
    description = {}
    key = ' Upper Limit'
    for c, attr in nominal_attrs.items():
        dispatch_attr = 'p0' if c in ['Line', 'Transformer', 'Link'] else attr[0]
        description[c + key] = pd.Series({'min':
                               (n.df(c)[attr + '_opt'] *
                               get_as_dense(n, c, attr[0] + '_max_pu') -
                               n.pnl(c)[dispatch_attr]).min().min()})
    return pd.concat(description, axis=1)

def define_objective(n, sns):
    """
    Defines and writes out the objective function

    """
    # constant for already done investment
    nom_attr = nominal_attrs.items()
    constant = 0
    for c, attr in nom_attr:
        ext_i = get_extendable_i(n, c)
        constant += n.df(c)[attr][ext_i] @ n.df(c).capital_cost[ext_i]
    object_const = write_bound(n, constant, constant)
    n.objective_f.write(linexpr((-1, object_const), as_pandas=False)[0])

    for c, attr in lookup.query('marginal_cost').index:
        cost = (get_as_dense(n, c, 'marginal_cost', sns)
                .loc[:, lambda ds: (ds != 0).all()]
                .mul(n.snapshot_weightings[sns], axis=0))
        if cost.empty: continue
        terms = linexpr((cost, get_var(n, c, attr).loc[sns, cost.columns]))
        n.objective_f.write(join_exprs(terms))
    # investment
    for c, attr in nominal_attrs.items():