How to use the causallib.simulation.CausalSimulator3.CausalSimulator3._discretize_col function in causallib

A DataFrame shaped (num_samples x num_of_possible_treatment_categories).

        Raises:
            ValueError: If given more than to categories. This method supports dichotomous treatment only.
        """
        if prob_category.size != 2:  # this method suited for dichotomous outcome only
            raise ValueError("logistic method supports only binary treatment. Got the distribution vector "
                             "{p_vec} of length {n_cat}".format(n_cat=prob_category.size, p_vec=prob_category))
        index_names = x_continuous.index
        columns_names = prob_category.index
        propensity = pd.DataFrame(index=index_names, columns=columns_names)
        # compute propensities:
        t = stats.norm(loc=0, scale=1).ppf(prob_category.iloc[1])  # percentile given a distribution
        cur_propensity = stats.norm(loc=x_continuous, scale=(1 - snr)).sf(t)  # sf is 1 - CDF
        # discretize values:
        treatment = CausalSimulator3._discretize_col(x_continuous, prob_category)
        propensity.loc[:, columns_names[1]] = cur_propensity
        propensity.loc[:, columns_names[0]] = np.ones(cur_propensity.size) - cur_propensity
        return propensity, treatment