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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):
 r"""Implied timescales of the TICA transformation For each :math:`i`-th eigenvalue, this returns .. math:: t_i = -\frac{\tau}{\log(|\lambda_i|)} where :math:`\tau` is the :py:obj:`lag` of the TICA object and :math:`\lambda_i` is the `i`-th :py:obj:`eigenvalue <eigenvalues>` of the TICA object. Returns ------- timescales: 1D np.array numpy array with the implied timescales. In principle, one should expect as many timescales as input coordinates were available. However, less eigenvalues will be returned if the TICA matrices were not full rank or :py:obj:`var_cutoff` was parsed """

        return -self.lag / np.log(np.abs(self.eigenvalues))