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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 canonical(self):
 """ Compute canonical preference representation Uses auxiliary problem of 9.4.2, with the preference shock process reintroduced Calculates pihat, llambdahat and ubhat for the equivalent canonical household technology """

        Ac1 = np.hstack((self.deltah, np.zeros((self.nh, self.nz))))
        Ac2 = np.hstack((np.zeros((self.nz, self.nh)), self.a22))
        Ac = np.vstack((Ac1, Ac2))
        Bc = np.vstack((self.thetah, np.zeros((self.nz, self.nc))))
        Cc = np.vstack((np.zeros((self.nh, self.nw)), self.c2))
        Rc1 = np.hstack((self.llambda.T.dot(self.llambda), -
                         self.llambda.T.dot(self.ub)))
        Rc2 = np.hstack((-self.ub.T.dot(self.llambda), self.ub.T.dot(self.ub)))
        Rc = np.vstack((Rc1, Rc2))
        Qc = self.pih.T.dot(self.pih)
        Nc = np.hstack(
            (self.pih.T.dot(self.llambda), -self.pih.T.dot(self.ub)))

        lq_aux = LQ(Qc, Rc, Ac, Bc, N=Nc, beta=self.beta)

        P1, F1, d1 = lq_aux.stationary_values()

        self.F_b = F1[:, 0:self.nh]
        self.F_f = F1[:, self.nh:]

        self.pihat = np.linalg.cholesky(self.pih.T.dot(
            self.pih) + self.beta.dot(self.thetah.T).dot(P1[0:self.nh, 0:self.nh]).dot(self.thetah)).T
        self.llambdahat = self.pihat.dot(self.F_b)
        self.ubhat = - self.pihat.dot(self.F_f)

        return