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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 stationary_distribution_from_backward_iteration(P, eps=1e-15):
 r"""Fast computation of the stationary vector using backward iteration. Parameters P : (M, M) scipy.sparse matrix Transition matrix eps : float (optional) Perturbation parameter for the true eigenvalue. Returns ------- pi : (M,) ndarray Stationary vector """

    A = P.transpose()
    mu = 1.0 - eps
    x0 = np.ones(P.shape[0])
    y = backward_iteration(A, mu, x0)
    pi = y / y.sum()
    return pi