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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 filtered_to_forecast(self):
 """ Updates the moments of the time t filtering distribution to the moments of the predictive distribution, which becomes the time t+1 prior """

        # === simplify notation === #
        A, C = self.ss.A, self.ss.C
        Q = np.dot(C, C.T)

        # === and then update === #
        self.x_hat = dot(A, self.x_hat)
        self.Sigma = dot(A, dot(self.Sigma, A.T)) + Q