code: def custom_covariance(self,beta):
                   
  
          cov_matrix = np.zeros((self.ylen,self.ylen))
          parm = np.array([self.latent_variables.z_list[k].prior.transform(beta[k]) for k in range(beta.shape[0])])
          return custom_covariance_matrix(cov_matrix, self.ylen, self.lags, parm)
docs: Creates Covariance Matrix for a given Beta Vector
          (Not necessarily the OLS covariance)
  
          Parameters
          ----------
          beta : np.array
              Contains untransformed starting values for latent variables
  
          Returns
          ----------
          A Covariance Matrix