def __init__(self, prior_lambda=None, prior_strength=1.0):
        """
        Continuous Bernoulli distribution.

        https://en.wikipedia.org/wiki/Continuous_Bernoulli_distribution

        - Univariate continuous distribution.
        - Support: open interval :math:`(0, 1)`.
        - Parameter: shape :math:`\\lambda \\in (0,1)`

        Parameters
        ----------
        prior_lambda
            shape :math:`\\lambda` of the prior Continuous Bernoulli distribution (default: None)
        prior_strength
            strength of the loss regularisation induced by the prior
        """
        self.prior_lambda = prior_lambda
        _check_in_open_0_1_intvl(self.prior_lambda, "lambda")

        self.sigmoid = nn.Sigmoid()
        super().__init__(
            likelihood_type=LikelihoodType.ContinuousBernoulli,
            parameter_names=["lambda"],
            prior_strength=prior_strength,
        )

        Continuous Bernoulli distribution.

        https://en.wikipedia.org/wiki/Continuous_Bernoulli_distribution

        - Univariate continuous distribution.
        - Support: open interval :math:`(0, 1)`.
        - Parameter: shape :math:`\lambda \in (0,1)`

        Parameters
        ----------
        prior_lambda
            shape :math:`\lambda` of the prior Continuous Bernoulli distribution (default: None)
        prior_strength
            strength of the loss regularisation induced by the prior
        
darts/utils/likelihood_models/torch.py