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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, reversible=False, mu=None):
 r"""Compute eigenvalues of given transition matrix. Parameters T : (d, d) ndarray Transition matrix (stochastic matrix) k : int or tuple of ints, optional Compute the first k eigenvalues of T reversible : bool, optional Indicate that transition matrix is reversible mu : (d,) ndarray, optional Stationary distribution of T Returns ------- eig : (n,) ndarray, The eigenvalues of T ordered with decreasing absolute value. If k is None then n=d, if k is int then n=k otherwise n is the length of the given tuple of eigenvalue indices. Notes ----- Eigenvalues are computed using the numpy.linalg interface for the corresponding LAPACK routines. 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 reversible:
        try:
            evals = eigenvalues_rev(T, k=k, mu=mu)
        except:
            evals = eigvals(T).real  # use fallback code but cast to real
    else:
        evals = eigvals(T)  # nonreversible

    """Sort by decreasing absolute value"""
    ind = np.argsort(np.abs(evals))[::-1]
    evals = evals[ind]

    if isinstance(k, (list, set, tuple)):
        try:
            return [evals[n] for n in k]
        except IndexError:
            raise ValueError("given indices do not exist: ", k)
    elif k is not None:
        return evals[: k]
    else:
        return evals