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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 update(self, z):
 """ Add a new measurement `z` to the H-Infinity filter. If `z` is None, nothing is changed. Parameters z : ndarray measurement for this update. """


        if z is None:
            return

        # rename for readability and a tiny extra bit of speed
        I = self._I
        gamma = self.gamma
        Q = self.Q
        H = self.H
        P = self.P
        x = self.x
        V_inv = self._V_inv
        F = self.F
        W = self.W

        # common subexpression H.T * V^-1
        HTVI = dot(H.T, V_inv)

        L = linalg.inv(I - gamma * dot(Q, P) + dot(HTVI, H).dot(P))

        # common subexpression P*L
        PL = dot(P, L)

        K = dot(F, PL).dot(HTVI)

        self.y = z - dot(H, x)

        # x = x + Ky
        # predict new x with residual scaled by the H-Infinity gain
        self.x = self.x + dot(K, self.y)
        self.P = dot(F, PL).dot(F.T) + W

        # force P to be symmetric
        self.P = (self.P + self.P.T) / 2

        # pylint: disable=bare-except
        try:
            self.z = np.copy(z)
        except:
            self.z = copy.deepcopy(z)