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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 calc_abc_interval(self, conf_percentage, init_vals, epsilon=0.001, **fit_kwargs):
 """ Calculates Approximate Bootstrap Confidence Intervals for one's model. Parameters conf_percentage : scalar in the interval (0.0, 100.0). Denotes the confidence-level for the returned endpoints. For instance, to calculate a 95% confidence interval, pass `95`. init_vals : 1D ndarray. The initial values used to estimate the one's choice model. epsilon : positive float, optional. Should denote the 'very small' value being used to calculate the desired finite difference approximations to the various influence functions. Should be close to zero. Default == sys.float_info.epsilon. fit_kwargs : additional keyword arguments, optional. Should contain any additional kwargs used to alter the default behavior of `model_obj.fit_mle` and thereby enforce conformity with how the MLE was obtained. Will be passed directly to `model_obj.fit_mle`. Returns ------- None. Will store the ABC intervals as `self.abc_interval`. """

        print("Calculating Approximate Bootstrap Confidence (ABC) Intervals")
        print(time.strftime("%a %m-%d-%Y %I:%M%p"))
        sys.stdout.flush()
        # Get the alpha % that corresponds to the given confidence percentage.
        alpha = bc.get_alpha_from_conf_percentage(conf_percentage)
        # Create the column names for the dataframe of confidence intervals
        single_column_names =\
            ['{:.3g}%'.format(alpha / 2.0),
             '{:.3g}%'.format(100 - alpha / 2.0)]
        # Calculate the ABC confidence intervals
        conf_intervals =\
            abc.calc_abc_interval(self.model_obj,
                                  self.mle_params.values,
                                  init_vals,
                                  conf_percentage,
                                  epsilon=epsilon,
                                  **fit_kwargs)
        # Store the ABC confidence intervals
        self.abc_interval = pd.DataFrame(conf_intervals.T,
                                         index=self.mle_params.index,
                                         columns=single_column_names)
        return None