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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 eigb(A, y0, eps, rmax=150, nswp=20, max_full_size=1000, verb=1):
 """ Approximate computation of minimal eigenvalues in tensor train format This function uses alternating least-squares algorithm for the computation of several minimal eigenvalues. If you want maximal eigenvalues, just send -A to the function. :Reference: S. V. Dolgov, B. N. Khoromskij, I. V. Oseledets, and D. V. Savostyanov. Computation of extreme eigenvalues in higher dimensions using block tensor train format. Computer Phys. Comm., 185(4):
1207-1216, 2014. http://dx.doi.org/10.1016/j.cpc.2013.12.017 :param A: Matrix in the TT-format :type A: matrix :param y0: Initial guess in the block TT-format, r(d+1) is the number of eigenvalues sought :type y0: tensor :param eps: Accuracy required :type eps: float :param rmax: Maximal rank :type rmax: int :param kickrank: Addition rank, the larger the more robus the method, :type kickrank: int :rtype: A tuple (ev, tensor), where ev is a list of eigenvalues, tensor is an approximation to eigenvectors. :Example: Solving a block eigenvalue problem Looking for 8 eigenvalues with accuracy 1E-06 swp: 1 er = 35.93 rmax:19 swp: 2 er = 4.51015E-04 rmax:18 swp: 3 er = 1.87584E-12 rmax:17 Total number of matvecs: 0 [ 0.00044828 0.00089654 0.00089654 0.00089654 0.0013448 0.0013448 0.0013448 0.00164356] """

    ry = y0.r.copy()
    lam = tt_eigb.tt_block_eig.tt_eigb(y0.d, A.n, A.m, A.tt.r, A.tt.core, y0.core, ry, eps,
                                       rmax, ry[y0.d], 0, nswp, max_full_size, verb)
    y = tensor()
    y.d = y0.d
    y.n = A.n.copy()
    y.r = ry
    y.core = tt_eigb.tt_block_eig.result_core.copy()
    tt_eigb.tt_block_eig.deallocate_result()
    y.get_ps()
    return y, lam