How to use the fitter.Fitter function in fitter

def _leaderboard_compute_overall_score(self, N=100):
        """Based on NULL distribution, compute overall score of model1

        Not finalised.

        """
        self._compute_pvalues_pred1(N=N)
        self._compute_pvalues_param1(N=N)
        import fitter
        fit_param1 = fitter.Fitter(self.rdistance_param1)
        fit_param1.distributions = ['beta']
        fit_param1.fit()
        fit_pred1 = fitter.Fitter(self.rdistance_pred1)
        fit_pred1.distributions = ['beta']
        fit_pred1.fit()

        import scipy.stats
        self.pvalues_param1 = scipy.stats.beta.cdf(self.scores['param1'].scores,
                *fit_param1.fitted_param['beta'])
        self.pvalues_pred1 = scipy.stats.beta.cdf(self.scores['pred1'].scores,
                *fit_pred1.fitted_param['beta'])

        self.scores['pred1']['pvalues'] = self.pvalues_pred1
        self.scores['param1']['pvalues'] = self.pvalues_param1

def _leaderboard_compute_overall_score(self, N=100):
        """Based on NULL distribution, compute overall score of model1

        Not finalised.

        """
        self._compute_pvalues_pred1(N=N)
        self._compute_pvalues_param1(N=N)
        import fitter
        fit_param1 = fitter.Fitter(self.rdistance_param1)
        fit_param1.distributions = ['beta']
        fit_param1.fit()
        fit_pred1 = fitter.Fitter(self.rdistance_pred1)
        fit_pred1.distributions = ['beta']
        fit_pred1.fit()

        import scipy.stats
        self.pvalues_param1 = scipy.stats.beta.cdf(self.scores['param1'].scores,
                *fit_param1.fitted_param['beta'])
        self.pvalues_pred1 = scipy.stats.beta.cdf(self.scores['pred1'].scores,
                *fit_pred1.fitted_param['beta'])

        self.scores['pred1']['pvalues'] = self.pvalues_pred1
        self.scores['param1']['pvalues'] = self.pvalues_param1