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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 _lim_moment(self, u, order=1):
 """ This method calculates the kth order limiting moment of the distribution. It is given by - E(u) = Integral (-inf to u) [ (x^k)*pdf(x) dx ] + (u^k)(1-cdf(u)) where, pdf is the probability density function and cdf is the cumulative density function of the distribution. Reference --------- Klugman, S. A., Panjer, H. H. and Willmot, G. E., Loss Models, From Data to Decisions, Fourth Edition, Wiley, definition 3.5 and equation 3.8. Parameters u: float The point at which the moment is to be calculated. order: int The order of the moment, default is first order. """

        def fun(x):
            return np.power(x, order) * self.factor.pdf(x)
        return (integrate.quad(fun, -np.inf, u)[0] +
                np.power(u, order)*(1 - self.factor.cdf(u)))