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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 _general_approximating_model(self, beta, T, Z, R, Q, h_approx):
 """ Creates simplest kind of approximating Gaussian model Parameters beta : np.array Contains untransformed starting values for latent variables T, Z, R, Q : np.array State space matrices used in KFS algorithm h_approx : float Value to use for the H matrix Returns H : np.array Approximating measurement variance matrix mu : np.array Approximating measurement constants """
     

        H = np.ones(self.data_length)*h_approx
        mu = np.zeros(self.data_length)

        return H, mu