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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 eig_corr(C0, Ct, epsilon=1e-10, method='QR', sign_maxelement=False):
 r""" Solve generalized eigenvalue problem with correlation matrices C0 and Ct Numerically robust solution of a generalized Hermitian (symmetric) eigenvalue problem of the form .. math:: \mathbf{C}_t \mathbf{r}_i = \mathbf{C}_0 \mathbf{r}_i l_i Computes :math:`m` dominant eigenvalues :math:`l_i` and eigenvectors :math:`\mathbf{r}_i`, where :math:`m` is the numerical rank of the problem. This is done by first conducting a Schur decomposition of the symmetric positive matrix :math:`\mathbf{C}_0`, then truncating its spectrum to retain only eigenvalues that are numerically greater than zero, then using this decomposition to define an ordinary eigenvalue Problem for :math:`\mathbf{C}_t` of size :math:`m`, and then solving this eigenvalue problem. Parameters C0 : ndarray (n,n) time-instantaneous correlation matrix. Must be symmetric positive definite Ct : ndarray (n,n) time-lagged correlation matrix. Must be symmetric epsilon : float eigenvalue norm cutoff. Eigenvalues of C0 with norms <= epsilon will be cut off. The remaining number of Eigenvalues define the size of the output. method : str Method to perform the decomposition of :math:`W` before inverting. Options are: * 'QR': QR-based robust eigenvalue decomposition of W * 'schur': Schur decomposition of W sign_maxelement : bool If True, re-scale each eigenvector such that its entry with maximal absolute value is positive. Returns ------- l : ndarray (m) The first m generalized eigenvalues, sorted by descending norm R : ndarray (n,m) The first m generalized eigenvectors, as a column matrix. """

    L = spd_inv_split(C0, epsilon=epsilon, method=method, canonical_signs=True)
    Ct_trans = _np.dot(_np.dot(L.T, Ct), L)

    # solve the symmetric eigenvalue problem in the new basis
    if _np.allclose(Ct.T, Ct):
        from scipy.linalg import eigh
        l, R_trans = eigh(Ct_trans)
    else:
        from scipy.linalg import eig
        l, R_trans = eig(Ct_trans)

    # sort eigenpairs
    l, R_trans = sort_by_norm(l, R_trans)

    # transform the eigenvectors back to the old basis
    R = _np.dot(L, R_trans)

    # Change signs of eigenvectors:
    if sign_maxelement:
        for j in range(R.shape[1]):
            imax = _np.argmax(_np.abs(R[:, j]))
            R[:, j] *= _np.sign(R[imax, j])

    # return result
    return l, R