def __init__(self):
        r"""
        Regression probabilistic output layers class. It characterizes the probability distribution of a target
        variable given a calibrated output as a Gaussian distribution. That is
        :math:`p(y|\mu, \sigma^2)=\text{Normal}(y|\mu, \sigma^2)`, where :math:`y` denotes a target variable and
        :math:`\omega=[\mu, \log\sigma^2]` a calibrated output.

        """
        super().__init__()
        self.log2pi = jnp.log(2 * jnp.pi)

        Regression probabilistic output layers class. It characterizes the probability distribution of a target
        variable given a calibrated output as a Gaussian distribution. That is
        :math:`p(y|\mu, \sigma^2)=\text{Normal}(y|\mu, \sigma^2)`, where :math:`y` denotes a target variable and
        :math:`\omega=[\mu, \log\sigma^2]` a calibrated output.

        
fortuna/prob_output_layer/regression.py