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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def second_order_score(y, mean, scale, shape, skewness):
 """ GAS t Update term potentially using second-order information - native Python function Parameters y : float datapoint for the time series mean : float location parameter for the t distribution scale : float scale parameter for the t distribution shape : float tail thickness parameter for the t distribution skewness : float skewness parameter for the t distribution Returns - Adjusted score of the t family """

        return ((shape+1)/shape)*(y-mean)/(np.power(scale,2) + (np.power(y-mean,2)/shape))/((shape+1)*((np.power(scale,2)*shape) - np.power(y-mean,2))/np.power((np.power(scale,2)*shape) + np.power(y-mean,2),2))