def __init__(self, log_var: Optional[float] = 0.0):
        """
        A diagonal Gaussian prior class.

        Parameters
        ----------
        log_var : Optional[float]
            Prior log-variance value. The covariance matrix of the prior distribution is given by a diagonal matrix
            with this parameter on every entry of the diagonal.
        """
        super().__init__()
        self.log_var = log_var
        self.prec = jnp.exp(-self.log_var)
        self.std = jnp.exp(0.5 * self.log_var)
        self.log2pi = jnp.log(2 * jnp.pi)

        A diagonal Gaussian prior class.

        Parameters
        ----------
        log_var : Optional[float]
            Prior log-variance value. The covariance matrix of the prior distribution is given by a diagonal matrix
            with this parameter on every entry of the diagonal.
        
fortuna/prob_model/prior/gaussian.py