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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 timescales(T, tau=1, k=None, ncv=None, reversible=False, mu=None):
 r"""Compute implied time scales of given transition matrix. Parameters T : (M, M) ndarray or scipy.sparse matrix Transition matrix tau : int (optional) The time-lag (in elementary time steps of the microstate trajectory) at which the given transition matrix was constructed. k : int (optional) Compute the first `k` implied time scales. ncv : int (optional, for sparse T only) 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 ------- ts : (M,) ndarray The implied time scales of the transition matrix. If `k` is not None then the shape of `ts` is (k,). Notes ----- The implied time scale :math:`t_i` is defined as .. math:: t_i=-\frac{\tau}{\log \lvert \lambda_i \rvert} 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. Examples -------- array([ inf, 9.49122158, 0.43429448]) """

    T = _types.ensure_ndarray_or_sparse(T, ndim=2, uniform=True, kind='numeric')
    if _issparse(T):
        return sparse.decomposition.timescales(T, tau=tau, k=k, ncv=ncv,
                                               reversible=reversible, mu=mu)
    else:
        return dense.decomposition.timescales(T, tau=tau, k=k, reversible=reversible, mu=mu)