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

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

        pr1 = self._eta * self.underlying_model.likelihood(
            np.array([1], dtype='uint'),
            modelparams,
            expparams
        )[0, :, :]

        # Now we concatenate over outcomes.
        L = qi.FiniteOutcomeModel.pr0_to_likelihood_array(outcomes, 1 - pr1)
        assert not np.any(np.isnan(L))
        return L

def __init__(self, freq_min=0.0, freq_max=1.0, n_particles=1000, noise="Absent", eta=1.0):

        base_model = ExpDecoKnownPrecessionModel(min_freq=freq_min)

        if noise is "Absent":
            self.model = base_model
        elif noise is "Binomial":
            self.model = qi.BinomialModel(base_model)
        elif noise is "Unbalanced":
            self.model = qi.BinomialModel(AsymmetricLossModel(base_model, eta=eta))

        self.n_particles = n_particles

        self.freq_min = freq_min
        self.freq_max = freq_max

        self.fft_est = None
        self.bay_est = None