How to use the causalml.inference.tree.models.DecisionTree function in causalml

min_samples_treatment: int, optional (default=10)
            The minimum number of samples required of the experiment group to be split at a leaf node.
        n_reg: int, optional (default=10)
            The regularization parameter defined in Rzepakowski et al. 2012,
            the weight (in terms of sample size) of the parent node influence
            on the child node, only effective for 'KL', 'ED', 'Chi', 'CTS' methods.
        parentNodeSummary : dictionary, optional (default = None)
            Node summary statistics of the parent tree node.

        Returns
        -------
        object of DecisionTree class
        '''

        if len(X) == 0:
            return DecisionTree()

        # Current Node Info and Summary
        currentNodeSummary = self.tree_node_summary(treatment, y,
                                                    min_samples_treatment=min_samples_treatment,
                                                    n_reg=n_reg,
                                                    parentNodeSummary=parentNodeSummary)
        if evaluationFunction == self.evaluate_CTS:
            currentScore = evaluationFunction(currentNodeSummary)
        else:
            currentScore = evaluationFunction(currentNodeSummary, control_name=self.control_name)

        # Prune Stats
        maxAbsDiff = 0
        maxDiff = -1.
        bestTreatment = self.control_name
        suboptTreatment = self.control_name

*best_set_right, evaluationFunction, max_depth, min_samples_leaf,
                depth + 1, min_samples_treatment=min_samples_treatment,
                n_reg=n_reg, parentNodeSummary=currentNodeSummary
            )

            return DecisionTree(
                col=bestAttribute[0], value=bestAttribute[1],
                trueBranch=trueBranch, falseBranch=falseBranch, summary=dcY,
                maxDiffTreatment=maxDiffTreatment, maxDiffSign=maxDiffSign,
                nodeSummary=currentNodeSummary,
                backupResults=self.uplift_classification_results(treatment, y),
                bestTreatment=bestTreatment, upliftScore=upliftScore
            )
        else:
            if evaluationFunction == self.evaluate_CTS:
                return DecisionTree(
                    results=self.uplift_classification_results(treatment, y),
                    summary=dcY, nodeSummary=currentNodeSummary,
                    bestTreatment=bestTreatment, upliftScore=upliftScore
                )
            else:
                return DecisionTree(
                    results=self.uplift_classification_results(treatment, y),
                    summary=dcY, maxDiffTreatment=maxDiffTreatment,
                    maxDiffSign=maxDiffSign, nodeSummary=currentNodeSummary,
                    bestTreatment=bestTreatment, upliftScore=upliftScore
                )

dcY['upliftScore'] = [round(upliftScore[0], 4), round(upliftScore[1], 4)]
        dcY['matchScore'] = round(upliftScore[0], 4)

        if bestGain > 0 and depth < max_depth:
            trueBranch = self.growDecisionTreeFrom(
                *best_set_left, evaluationFunction, max_depth, min_samples_leaf,
                depth + 1, min_samples_treatment=min_samples_treatment,
                n_reg=n_reg, parentNodeSummary=currentNodeSummary
            )
            falseBranch = self.growDecisionTreeFrom(
                *best_set_right, evaluationFunction, max_depth, min_samples_leaf,
                depth + 1, min_samples_treatment=min_samples_treatment,
                n_reg=n_reg, parentNodeSummary=currentNodeSummary
            )

            return DecisionTree(
                col=bestAttribute[0], value=bestAttribute[1],
                trueBranch=trueBranch, falseBranch=falseBranch, summary=dcY,
                maxDiffTreatment=maxDiffTreatment, maxDiffSign=maxDiffSign,
                nodeSummary=currentNodeSummary,
                backupResults=self.uplift_classification_results(treatment, y),
                bestTreatment=bestTreatment, upliftScore=upliftScore
            )
        else:
            if evaluationFunction == self.evaluate_CTS:
                return DecisionTree(
                    results=self.uplift_classification_results(treatment, y),
                    summary=dcY, nodeSummary=currentNodeSummary,
                    bestTreatment=bestTreatment, upliftScore=upliftScore
                )
            else:
                return DecisionTree(

col=bestAttribute[0], value=bestAttribute[1],
                trueBranch=trueBranch, falseBranch=falseBranch, summary=dcY,
                maxDiffTreatment=maxDiffTreatment, maxDiffSign=maxDiffSign,
                nodeSummary=currentNodeSummary,
                backupResults=self.uplift_classification_results(treatment, y),
                bestTreatment=bestTreatment, upliftScore=upliftScore
            )
        else:
            if evaluationFunction == self.evaluate_CTS:
                return DecisionTree(
                    results=self.uplift_classification_results(treatment, y),
                    summary=dcY, nodeSummary=currentNodeSummary,
                    bestTreatment=bestTreatment, upliftScore=upliftScore
                )
            else:
                return DecisionTree(
                    results=self.uplift_classification_results(treatment, y),
                    summary=dcY, maxDiffTreatment=maxDiffTreatment,
                    maxDiffSign=maxDiffSign, nodeSummary=currentNodeSummary,
                    bestTreatment=bestTreatment, upliftScore=upliftScore
                )