code: def second_order_score(y, mean, scale, shape, skewness):
          
          return ((y-mean)/float(scale*np.abs(y-mean))) / (-(np.power(y-mean,2) - np.power(np.abs(mean-y),2))/(scale*np.power(np.abs(mean-y),3)))
docs: GAS Laplace Update term potentially using second-order information - native Python function
  
          Parameters
          ----------
          y : float
              datapoint for the time series
  
          mean : float
              location parameter for the Laplace distribution
  
          scale : float
              scale parameter for the Laplace distribution
  
          shape : float
              tail thickness parameter for the Laplace distribution
  
          skewness : float
              skewness parameter for the Laplace distribution
  
          Returns
          ----------
          - Adjusted score of the Laplace family