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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 angles(F, G, ip_B=None, compute_vectors=False):
 """Principal angles between two subspaces. This algorithm is based on algorithm 6.2 in `Knyazev, Argentati. Principal angles between subspaces in an A-based scalar product: algorithms and perturbation estimates. 2002.` This algorithm can also handle small angles (in contrast to the naive cosine-based svd algorithm). :param F: array with ``shape==(N,k)``. :param G: array with ``shape==(N,l)``. :param ip_B: (optional) angles are computed with respect to this inner product. See :py:meth:`inner`. :param compute_vectors: (optional) if set to ``False`` then only the angles are returned (default). If set to ``True`` then also the principal vectors are returned. :return: * ``theta`` if ``compute_vectors==False`` * ``theta, U, V`` if ``compute_vectors==True`` where * ``theta`` is the array with ``shape==(max(k,l),)`` containing the principal angles :math:`0\\leq\\theta_1\\leq\\ldots\\leq\\theta_{\\max\\{k,l\\}}\\leq \\frac{\\pi}{2}`. * ``U`` are the principal vectors from F with :math:`\\langle U,U \\rangle=I_k`. * ``V`` are the principal vectors from G with :math:`\\langle V,V \\rangle=I_l`. The principal angles and vectors fulfill the relation :math:`\\langle U,V \\rangle = \ \\begin{bmatrix} \ \\cos(\\Theta) & 0_{m,l-m} \\\\ \ 0_{k-m,m} & 0_{k-m,l-m} \ \\end{bmatrix}` where :math:`m=\\min\\{k,l\\}` and :math:`\\cos(\\Theta)=\\operatorname{diag}(\\cos(\\theta_1),\\ldots,\\cos(\\theta_m))`. Furthermore, :math:`\\theta_{m+1}=\\ldots=\\theta_{\\max\\{k,l\\}}=\\frac{\\pi}{2}`. """

    # make sure that F.shape[1]>=G.shape[1]
    reverse = False
    if F.shape[1] < G.shape[1]:
        reverse = True
        F, G = G, F

    QF, _ = qr(F, ip_B=ip_B)
    QG, _ = qr(G, ip_B=ip_B)

    # one or both matrices empty? (enough to check G here)
    if G.shape[1] == 0:
        theta = numpy.ones(F.shape[1])*numpy.pi/2
        U = QF
        V = QG
    else:
        Y, s, Z = scipy.linalg.svd(inner(QF, QG, ip_B=ip_B))
        Vcos = numpy.dot(QG, Z.T.conj())
        n_large = numpy.flatnonzero((s**2) < 0.5).shape[0]
        n_small = s.shape[0] - n_large
        theta = numpy.r_[
            numpy.arccos(s[n_small:]),  # [-i:] does not work if i==0
            numpy.ones(F.shape[1]-G.shape[1])*numpy.pi/2]
        if compute_vectors:
            Ucos = numpy.dot(QF, Y)
            U = Ucos[:, n_small:]
            V = Vcos[:, n_small:]

        if n_small > 0:
            RG = Vcos[:, :n_small]
            S = RG - numpy.dot(QF, inner(QF, RG, ip_B=ip_B))
            _, R = qr(S, ip_B=ip_B)
            Y, u, Z = scipy.linalg.svd(R)
            theta = numpy.r_[
                numpy.arcsin(u[::-1][:n_small]),
                theta]
            if compute_vectors:
                RF = Ucos[:, :n_small]
                Vsin = numpy.dot(RG, Z.T.conj())
                # next line is hand-crafted since the line from the paper does
                # not seem to work.
                Usin = numpy.dot(RF, numpy.dot(
                    numpy.diag(1/s[:n_small]),
                    numpy.dot(Z.T.conj(), numpy.diag(s[:n_small]))))
                U = numpy.c_[Usin, U]
                V = numpy.c_[Vsin, V]

    if compute_vectors:
        if reverse:
            U, V = V, U
        return theta, U, V
    else:
        return theta