def METHOD_NAME(self, prior_dist):
    prior_prec = prior_dist.covariance_matrix.inverse()
    prior_mean = prior_dist.mean
    noise = torch.randn_like(prior_mean).mul_(self.mean_init_std)
    self.natural_vec.data.copy_((prior_prec @ prior_mean.unsqueeze(-1)).squeeze(-1).add_(noise))
    self.natural_mat.data.copy_(prior_prec.mul(-0.5))