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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 best_fit_likelihood(self):
 """ returns the log likelihood of the best fit model of the current state of this class :return: log likelihood, float """

        kwargs_lens, kwargs_source, kwargs_lens_light, kwargs_ps, kwargs_cosmo = self.best_fit(bijective=False)
        param_class = self._param_class
        likelihoodModule = self.likelihoodModule
        logL, _ = likelihoodModule.logL(param_class.kwargs2args(kwargs_lens, kwargs_source, kwargs_lens_light, kwargs_ps,
                                                             kwargs_cosmo))
        return logL