How to use the jupyter.Timo.own.mfl_sensing_simplelib.MultiHahnPGH function in jupyter

width_allow = 0.25 * tau_period_us

        is_flat_l = True
        while is_flat_l:
            if abs((tau % tau_period_us) - tau_period_us / 2) > width_allow:
                tau += tau_period_us / 10
            else:
                is_flat_l = False

        eps[self._t] = tau

        return eps


class T2_Thresh_MultiHahnPGH(MultiHahnPGH):

    def __init__(self, updater, B_gauss, tau_thresh_us, oplist=None, norm='Frobenius', inv_field='x_', t_field='t',
                 inv_func=identity,
                 t_func=identity,
                 maxiters=10,
                 other_fields=None
                 ):
        super().__init__(updater, B_gauss)
        self._updater = updater
        self._oplist = oplist
        self._norm = norm
        self._x_ = inv_field
        self._t = t_field
        self._inv_func = inv_func
        self._t_func = t_func
        self._maxiters = maxiters

self._other_fields = other_fields if other_fields is not None else {}
        self._b_gauss = B_gauss
        self._tau_thesh_us = tau_thresh_us

    def __call__(self):
        eps = super().__call__()
        # eps[self._t] = 100

        if eps[self._t] > self._tau_thesh_us:
            eps[self._t] = self._tau_thesh_us / 2

        # return eps
        return self.avoid_flat_likelihood(eps)


class T2RandPenalty_MultiHahnPGH(MultiHahnPGH):

    def __init__(self, updater, B_gauss, tau_thresh_rescale, oplist=None, norm='Frobenius', inv_field='x_', t_field='t',
                 inv_func=identity,
                 t_func=identity,
                 maxiters=10,
                 other_fields=None,
                 scale_f=2.0
                 ):
        super().__init__(updater, B_gauss)
        self._updater = updater
        self._oplist = oplist
        self._norm = norm
        self._x_ = inv_field
        self._t = t_field
        self._inv_func = inv_func
        self._t_func = t_func