def __init__(self, prior_xzero=None, prior_gamma=None, prior_strength=1.0):
        """
        Cauchy Distribution.

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

        - Univariate continuous distribution.
        - Support: :math:`\\mathbb{R}`.
        - Parameters: location :math:`x_0 \\in \\mathbb{R}`, scale :math:`\\gamma > 0`.

        Due to its fat tails, this distribution is typically harder to estimate,
        and your mileage may vary. Also be aware that it typically
        requires a large value for `num_samples` for sampling predictions.

        Parameters
        ----------
        prior_xzero
            location parameter :math:`x_0` of the Cauchy distribution (default: None)
        prior_gamma
            scale parameter :math:`\\gamma` of the Cauchy distribution, strictly positive (default: None)
        prior_strength
            strength of the loss regularisation induced by the prior
        """
        self.prior_xzero = prior_xzero
        self.prior_gamma = prior_gamma
        _check_strict_positive(self.prior_gamma, "gamma")

        self.softplus = nn.Softplus()
        super().__init__(
            likelihood_type=LikelihoodType.Cauchy,
            parameter_names=["xzero", "gamma"],
            prior_strength=prior_strength,
        )

        Cauchy Distribution.

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

        - Univariate continuous distribution.
        - Support: :math:`\mathbb{R}`.
        - Parameters: location :math:`x_0 \in \mathbb{R}`, scale :math:`\gamma > 0`.

        Due to its fat tails, this distribution is typically harder to estimate,
        and your mileage may vary. Also be aware that it typically
        requires a large value for `num_samples` for sampling predictions.

        Parameters
        ----------
        prior_xzero
            location parameter :math:`x_0` of the Cauchy distribution (default: None)
        prior_gamma
            scale parameter :math:`\gamma` of the Cauchy distribution, strictly positive (default: None)
        prior_strength
            strength of the loss regularisation induced by the prior
        
darts/utils/likelihood_models/torch.py