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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 eigenvalues(T, k=None, ncv=None, reversible=False, mu=None):
 r"""Compute the eigenvalues of a sparse transition matrix. Parameters T : (M, M) scipy.sparse matrix Transition matrix k : int, optional Number of eigenvalues to compute. ncv : int, optional The number of Lanczos vectors generated, `ncv` must be greater than k; it is recommended that ncv > 2*k reversible : bool, optional Indicate that transition matrix is reversible mu : (M,) ndarray, optional Stationary distribution of T Returns ------- v : (k,) ndarray Eigenvalues of T Notes ----- The first k eigenvalues of largest magnitude are computed. If reversible=True the the eigenvalues of the similar symmetric matrix `\sqrt(\mu_i / \mu_j) p_{ij}` will be computed. The precomputed stationary distribution will only be used if reversible=True. """

    if k is None:
        raise ValueError("Number of eigenvalues required for decomposition of sparse matrix")
    else:
        if reversible:
            try:
                v = eigenvalues_rev(T, k, ncv=ncv, mu=mu)
            except:  # use fallback code, but cast to real
                v = scipy.sparse.linalg.eigs(T, k=k, which='LM', return_eigenvectors=False, ncv=ncv).real
        else:
            v = scipy.sparse.linalg.eigs(T, k=k, which='LM', return_eigenvectors=False, ncv=ncv)

    ind = np.argsort(np.abs(v))[::-1]
    return v[ind]