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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_(self):
 """Implied relaxation timescales of the model. The relaxation of any initial distribution towards equilibrium is given, according to this model, by a sum of terms -- each corresponding to the relaxation along a specific direction (eigenvector) in state space -- which decay exponentially in time. See equation 19. from [1]. Returns ------- timescales : array-like, shape = (n_timescales,) The longest implied relaxation timescales of the model, expressed in units of time-step between indices in the source data supplied to ``fit()``. References .. [1] Prinz, Jan-Hendrik, et al. "Markov models of molecular kinetics: Generation and validation." J. Chem. Phys. 134.17 (2011):
 174105. """

        u, lv, rv = self._get_eigensystem()

        # make sure to leave off equilibrium distribution
        with np.errstate(invalid='ignore', divide='ignore'):
            timescales = - self.lag_time / np.log(u[1:])
        return timescales