def __init__(self, mu=0.0, sigma=1.0, lower=None, upper=None, transform=None, **kwargs):
        """
        Parameters
        ----------
        mu : float
            Mean parameter for the Truncated Normal distribution

        sigma : float
            Standard deviation for the Truncated Normal distribution

        lower: float
            Lower truncation for the distribution

        upper: float
            Upper truncation for the distribution

        transform : str
            Whether to apply a transformation - e.g. 'exp' or 'logit'
        """
        super(TruncatedNormal, self).__init__(transform)
        self.mu0 = mu
        self.sigma0 = sigma
        self.upper = upper
        self.lower = lower
        self.covariance_prior = False
def __init__ ( self , mu = 0.0 , sigma = 1.0 , lower = None , upper = None , transform = None , ** kwargs ) : super ( TruncatedNormal , self ) . __init__ ( transform ) self . mu0 = mu self . sigma0 = sigma self . upper = upper self . lower = lower self . covariance_prior = False
Parameters
        ----------
        mu : float
            Mean parameter for the Truncated Normal distribution

        sigma : float
            Standard deviation for the Truncated Normal distribution

        lower: float
            Lower truncation for the distribution

        upper: float
            Upper truncation for the distribution

        transform : str
            Whether to apply a transformation - e.g. 'exp' or 'logit'