code: def mb_neg_loglik(self, beta, mini_batch):
               
  
          lmda, Y, ___, theta = self._mb_model(beta, mini_batch)
          return -np.sum(logpdf(Y, self.latent_variables.z_list[-2].prior.transform(beta[-2]), 
              loc=theta, scale=np.exp(lmda/2.0), 
              skewness = self.latent_variables.z_list[-3].prior.transform(beta[-3])))
docs: Creates the negative log-likelihood of the model
  
          Parameters
          ----------
          beta : np.array
              Contains untransformed starting values for latent variables
  
          mini_batch : int
              Size of each mini batch of data
  
          Returns
          ----------
          The negative logliklihood of the model