def __init__(self, prior_alphas=None, prior_strength=1.0):
        """
        Dirichlet distribution.

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

        - Multivariate continuous distribution, modeling all components of a time series jointly.
        - Support: The :math:`K`-dimensional simplex for series of dimension :math:`K`, i.e.,
          :math:`x_1, ..., x_K \\text{ with } x_i \\in (0,1),\\; \\sum_i^K{x_i}=1`.
        - Parameter: concentrations :math:`\\alpha_1, ..., \\alpha_K` with :math:`\\alpha_i > 0`.

        Parameters
        ----------
        prior_alphas
            concentrations parameters :math:`\\alpha` of the prior Dirichlet distribution.
        prior_strength
            strength of the loss regularisation induced by the prior
        """
        self.prior_alphas = prior_alphas
        _check_strict_positive(self.prior_alphas)
        self.softmax = nn.Softmax(dim=2)
        super().__init__(
            likelihood_type=LikelihoodType.Dirichlet,
            parameter_names=["alpha"],
            prior_strength=prior_strength,
        )

        Dirichlet distribution.

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

        - Multivariate continuous distribution, modeling all components of a time series jointly.
        - Support: The :math:`K`-dimensional simplex for series of dimension :math:`K`, i.e.,
          :math:`x_1, ..., x_K \text{ with } x_i \in (0,1),\; \sum_i^K{x_i}=1`.
        - Parameter: concentrations :math:`\alpha_1, ..., \alpha_K` with :math:`\alpha_i > 0`.

        Parameters
        ----------
        prior_alphas
            concentrations parameters :math:`\alpha` of the prior Dirichlet distribution.
        prior_strength
            strength of the loss regularisation induced by the prior
        
darts/utils/likelihood_models/torch.py