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

(pd.Series, pd.DataFrame, pd.Series): 3-element tuple containing:

            - **treatment** (*pd.Series*): Treatment assignment to each sample.
            - **propensity** (*pd.DataFrame*): The marginal conditional probability of treatment given covariates.
                                               A DataFrame shaped (num_samples x num_of_possible_treatment_categories).
            - **beta** (*pd.Series*): The coefficients used to generate current variable from it predecessors.

        Raises:
            ValueError: if prob_category is None (treatment must be categorical)
            ValueError: If prob_category is not a legitimate probability vector (non negative, sums to 1)
        """
        # Check input validity:
        if prob_category is None:
            raise ValueError("Treatment variable must be categorical, therefore it must have a legitimate distribution "
                             "over its possible values. Got None instead.")
        CausalSimulator3._check_for_legitimate_probabilities(prob_category)

        # generate only the continuous signal since it is later processed (therefore prob_category = None)
        x_continuous, beta = self.generate_covariate_col(X_parents=X_parents, link_type=link_type, snr=snr,
                                                         prob_category=None, num_samples=X_parents.index.size,
                                                         var_name=var_name)

        generation_method = self.TREATMENT_METHODS.get(method)
        if generation_method is None:
            raise KeyError("The given method {method} is not supported, "
                           "only {valid_methods}.".format(valid_methods=list(self.TREATMENT_METHODS.keys()),
                                                          method=method))
        else:
            params = self.params.get(var_name, {})
            propensity, treatment = generation_method(x_continuous, prob_category, snr=snr, params=params)

        return treatment.astype(int), propensity.astype(float), beta

discretized values.

        Returns:
            res (pd.Series): A continuous covariate column if prob_category is None, else a discrete column according
                             to the given probabilities.
            bins (pd.Series): the bins

        Raises:
            ValueError: If prob_category is not a legitimate probability vector (non negative, sums to 1)
        """
        if prob_category is None or x_col.nunique() <= prob_category.size:
            res = x_col
            bins = None
        else:  # should perform discretization
            if bins is None:  # should create new bins
                CausalSimulator3._check_for_legitimate_probabilities(prob_category)
                # make k-1 thresholds (based on quantiles of the cdf, count how many thresholds each samples crosses:
                # see: https://en.wikipedia.org/wiki/Quantile_function#Definition
                if method == "gaussian":  # discretize according to percentiles drawn from normal distribution
                    bins = stats.norm(loc=0, scale=1).ppf(np.cumsum(prob_category)[:-1])
                    cutoffs = pd.DataFrame([x_col > thresh for thresh in bins]).T
                    res = cutoffs.sum(axis="columns")
                elif method == "empiric":  # discretize according to percentiles from the empirical data itself
                    try:
                        cumulative_ps = pd.Series(0, index=["null"]).append(prob_category).cumsum()
                        res, bins = pd.qcut(x=x_col, q=cumulative_ps,
                                            labels=prob_category.index, retbins=True)
                        bins = pd.Series(data=bins, index=cumulative_ps.index)
                        # TODO: maybe noise this a little?
                    except ValueError as _:
                        warnings.warn("Error occurred while discretizing column using pd.qcut. "
                                      "Probably the columns' values where already discrete (probably because it's "