class_docstr: Laplace Distribution
  ----
  This class contains methods relating to the Laplace distribution for time series.
class_docstr_tok: ["Laplace","Distribution","--","--","This","class","contains","methods","relating","to","the","Laplace","distribution","for","time","series","."]
docstr: 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
docstr_tok: ["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"]
code: def second_order_score(y, mean, scale, shape, skewness):
      """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
      """
      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))
      )
  
imports: import numpy
  import scipy.stats
  import scipy.special
  from .family import Family
  from .flat import Flat
  from .normal import Normal
  from .gas_recursions import gas_recursion_laplace_orderone, gas_recursion_laplace_ordertwo
  from .gas_recursions import gasx_recursion_laplace_orderone, gasx_recursion_laplace_ordertwo
  from .gas_recursions import gas_llev_recursion_laplace_orderone, gas_llev_recursion_laplace_ordertwo
  from .gas_recursions import gas_llt_recursion_laplace_orderone, gas_llt_recursion_laplace_ordertwo
  from .gas_recursions import gas_reg_recursion_laplace_orderone, gas_reg_recursion_laplace_ordertwo
label_desc: Use image node_id 13 for calling the Laplace obj's underlying member method code with example usage: obj.second_order_score(y, mean, scale, shape, skewness) and returns: unknown