def __init__(
        self,
        model: nn.Module,
        likelihood_log_variance_model: nn.Module,
        model_editor: Optional[ModelEditor] = None,
        seed: int = 0,
    ):
        r"""
        A calibration regressor class.

        Parameters
        ----------
        model : nn.Module
            A model describing the deterministic relation between inputs and outputs. It characterizes the mean model
            of the likelihood function. The outputs must belong to the same space as the target variables.
            Let :math:`x` be input variables and :math:`w` the random model parameters. Then the model is described by
            a function :math:`\mu(w, x)`.
        likelihood_log_variance_model: nn.Module
            A model characterizing the log-variance of a Gaussian likelihood function. The outputs must belong to the
            same space as the target variables. Let :math:`x` be input variables and :math:`w` the random model
            parameters. Then the model is described by a function :math:`\log\sigma^2(w, x)`.
        model_editor : ModelEditor
            A model_editor objects. It takes the forward pass and transforms the outputs.
        seed: int
            A random seed.

        Attributes
        ----------
        model : nn.Module
            See `model` in `Parameters`.
        model_manager : RegressionModelManager
            This object orchestrates the model's forward pass. Given a mean model :math:`\mu(w, x)` and a log-variance
            model :math:`\log\sigma^2`, the model manager concatenates the two into
            :math:`f(w, x)=[\mu(w, x), \log\sigma^2(w, x)]`.
        prob_output_layer : RegressionProbOutputLayer
            This object characterizes the distribution of the target variable given the calibrated outputs. It is
            defined by :math:`p(y|\omega)=\text{Categorical}(p=softmax(\omega))`, where :math:`\omega` denote the
            calibrated outputs and :math:`y` denotes a target variable.
        likelihood : RegressionLikelihood
            The likelihood function. This is defined by
            :math:`p(y|w, \phi, x) = \text{Categorical}(p=\text{softmax}(g(\phi, f(w, x)))`.
        predictive : RegressionPredictive
            This denotes the predictive distribution, that is :math:`p(y|\phi, x, \mathcal{D})`.
        """
        self.model_manager = RegressionModelManager(
            model, likelihood_log_variance_model, model_editor=model_editor
        )
        self.prob_output_layer = RegressionProbOutputLayer()
        self.likelihood = RegressionLikelihood(
            model_manager=self.model_manager,
            prob_output_layer=self.prob_output_layer,
            output_calib_manager=None,
        )
        self.predictive = RegressionPredictive(likelihood=self.likelihood)
        super().__init__(seed=seed)

        A calibration regressor class.

        Parameters
        ----------
        model : nn.Module
            A model describing the deterministic relation between inputs and outputs. It characterizes the mean model
            of the likelihood function. The outputs must belong to the same space as the target variables.
            Let :math:`x` be input variables and :math:`w` the random model parameters. Then the model is described by
            a function :math:`\mu(w, x)`.
        likelihood_log_variance_model: nn.Module
            A model characterizing the log-variance of a Gaussian likelihood function. The outputs must belong to the
            same space as the target variables. Let :math:`x` be input variables and :math:`w` the random model
            parameters. Then the model is described by a function :math:`\log\sigma^2(w, x)`.
        model_editor : ModelEditor
            A model_editor objects. It takes the forward pass and transforms the outputs.
        seed: int
            A random seed.

        Attributes
        ----------
        model : nn.Module
            See `model` in `Parameters`.
        model_manager : RegressionModelManager
            This object orchestrates the model's forward pass. Given a mean model :math:`\mu(w, x)` and a log-variance
            model :math:`\log\sigma^2`, the model manager concatenates the two into
            :math:`f(w, x)=[\mu(w, x), \log\sigma^2(w, x)]`.
        prob_output_layer : RegressionProbOutputLayer
            This object characterizes the distribution of the target variable given the calibrated outputs. It is
            defined by :math:`p(y|\omega)=\text{Categorical}(p=softmax(\omega))`, where :math:`\omega` denote the
            calibrated outputs and :math:`y` denotes a target variable.
        likelihood : RegressionLikelihood
            The likelihood function. This is defined by
            :math:`p(y|w, \phi, x) = \text{Categorical}(p=\text{softmax}(g(\phi, f(w, x)))`.
        predictive : RegressionPredictive
            This denotes the predictive distribution, that is :math:`p(y|\phi, x, \mathcal{D})`.
        
fortuna/calib_model/regression.py