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

# Allocating first serves to make sure that a shape mismatch later
        # will cause an error.
        pr0 = np.zeros((modelparams.shape[0], expparams.shape[0]))

        pr0[:, :] = 0.5 * (np.cos(t * modelparams[:, 0] / 2) ** 2 + np.cos(t * modelparams[:, 1] / 2) ** 2)[
            ..., np.newaxis]

        #         print("Pr0 = " + str(pr0) )

        # Now we concatenate over outcomes.
        # print("likelihoods: " + str(qi.FiniteOutcomeModel.pr0_to_likelihood_array(outcomes, pr0)))

        return qi.FiniteOutcomeModel.pr0_to_likelihood_array(outcomes, pr0)


class ExpDecoKnownMultimodePrecModel(MultimodePrecModel):

    def __init__(self, min_freq=0, inv_T2=0.):
        super().__init__()

        self._min_freq = min_freq
        self._invT2 = inv_T2

    def likelihood(self, outcomes, modelparams, expparams):
        """
        :param np.ndarray outcomes: set of possible experimental outcomes (here [0,1])
        :param np.ndarray modelparams: Set of model parameter vectors to be
                updated.
        :param np.ndarray expparams: An experiment parameter array describing
            the experiment that was just performed.

        :param numpy.ndarray: likelihoods of obtaining outcome ``0`` from each

def likelihood(self, outcomes, modelparams, expparams):
        # By calling the superclass implementation, we can consolidate
        # call counting there.
        super(MultimodePrecModel, self).likelihood(
            outcomes, modelparams, expparams
        )

        # print('outcomes = ' + repr(outcomes))

        # Possibly add a second axis to modelparams.
        if len(modelparams.shape) == 1:
            modelparams = modelparams[..., np.newaxis]

        #         print('modelparams = ' + repr(modelparams))
        # print('expparams = ' + repr(expparams))
        # print('m = ' + str(modelparams))
        # print('w_ = ' + str(expparams['w_']))

        t = expparams['t']
        # print('dw=' + repr(dw))