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

return tau_corrected_s

    def apply_penalty_randomized(self, tau_s):
        # randomly multiply scale with [0, 1)
        # avoid that all very big taus get mapped to a single value

        save_scale_f = self.scale_f
        rand = np.random.rand()
        self.scale_f *= rand
        tau_corrected_s = self.apply_penalty(tau_s)
        self.scale_f = save_scale_f

        return tau_corrected_s


class MultiHahnPGH(MultiPGH):

    def __init__(self, updater, B_gauss, oplist=None, norm='Frobenius', inv_field='x_', t_field='t',
                 inv_func=identity,
                 t_func=identity,
                 maxiters=10,
                 other_fields=None
                 ):
        super().__init__(updater)
        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

eps[self._t] = 1 / minsingvalnorm(deltaH)  # Min SingVal norm
            elif self._norm == 'SingVal':
                eps[self._t] = 1 / singvalnorm(deltaH)  # Max SingVal
            else:
                eps[self._t] = 1 / np.linalg.norm(deltaH)
                raise RuntimeError("Unknown Norm: using Frobenius norm instead")
        for field, value in self._other_fields.items():
            eps[field] = value

        return eps

    def norm_mean_projection(self, x, xp):
        return 0


class T2RandPenalty_MultiPGH(MultiPGH):
    def __init__(self, updater, tau_thresh_rescale, inv_field='x_', t_field='t',
                 inv_func=qi.expdesign.identity,
                 t_func=qi.expdesign.identity,
                 oplist=None,
                 maxiters=10,
                 other_fields=None, scale_f=2.0):
        """
        Apply a penalty on taus calculated from stdPGH and rescale them to lower values.
        :param tau_thresh_rescale: values above will be rescaled
        :param scale_f: controls the cut off tau. tau_cut = tau_thresh_rescale + scale_f * tau_thresh_rescale
                        = 2 -> tau_max = 3*tau_thresh_rescale
        """

        super().__init__(updater, oplist=oplist, inv_field=inv_field, t_field=t_field, inv_func=inv_func, t_func=t_func,
                         maxiters=maxiters, other_fields=other_fields)
        self.tau_thresh_rescale = tau_thresh_rescale