How to use the econml.ortho_forest.ContinuousTreatmentOrthoForest function in econml

def _pointwise_effect(self, X_single):
        """
        We need to post-process the parameters returned by the _pointwise_effect
        of the BaseOrthoForest class due to the local linear correction. The
        base class function will return the intercept and the coefficient of the
        local linear fit. We multiply it with the input co-variate to get the
        predicted effect.
        """
        parameter = super(ContinuousTreatmentOrthoForest, self)._pointwise_effect(X_single)
        X_aug = np.append([1], X_single)
        parameter = parameter.reshape((X_aug.shape[0], -1)).T
        return np.dot(parameter, X_aug)

self.model_Y = model_Y
        self.model_T_final = model_T_final
        self.model_Y_final = model_Y_final
        if self.model_T_final is None:
            self.model_T_final = clone(self.model_T, safe=False)
        if self.model_Y_final is None:
            self.model_Y_final = clone(self.model_Y, safe=False)
        # Define nuisance estimators
        nuisance_estimator = ContinuousTreatmentOrthoForest.nuisance_estimator_generator(
            self.model_T, self.model_Y, random_state, second_stage=False)
        second_stage_nuisance_estimator = ContinuousTreatmentOrthoForest.nuisance_estimator_generator(
            self.model_T_final, self.model_Y_final, random_state, second_stage=True)
        # Define parameter estimators
        parameter_estimator = ContinuousTreatmentOrthoForest.parameter_estimator_func
        second_stage_parameter_estimator =\
            ContinuousTreatmentOrthoForest.second_stage_parameter_estimator_gen(self.lambda_reg)
        # Define
        moment_and_mean_gradient_estimator = ContinuousTreatmentOrthoForest.moment_and_mean_gradient_estimator_func
        super(ContinuousTreatmentOrthoForest, self).__init__(
            nuisance_estimator,
            second_stage_nuisance_estimator,
            parameter_estimator,
            second_stage_parameter_estimator,
            moment_and_mean_gradient_estimator,
            n_trees=n_trees,
            min_leaf_size=min_leaf_size,
            max_depth=max_depth,
            subsample_ratio=subsample_ratio,
            bootstrap=bootstrap,
            n_jobs=n_jobs,
            random_state=random_state)

self.model_Y_final = model_Y_final
        if self.model_T_final is None:
            self.model_T_final = clone(self.model_T, safe=False)
        if self.model_Y_final is None:
            self.model_Y_final = clone(self.model_Y, safe=False)
        # Define nuisance estimators
        nuisance_estimator = ContinuousTreatmentOrthoForest.nuisance_estimator_generator(
            self.model_T, self.model_Y, random_state, second_stage=False)
        second_stage_nuisance_estimator = ContinuousTreatmentOrthoForest.nuisance_estimator_generator(
            self.model_T_final, self.model_Y_final, random_state, second_stage=True)
        # Define parameter estimators
        parameter_estimator = ContinuousTreatmentOrthoForest.parameter_estimator_func
        second_stage_parameter_estimator =\
            ContinuousTreatmentOrthoForest.second_stage_parameter_estimator_gen(self.lambda_reg)
        # Define
        moment_and_mean_gradient_estimator = ContinuousTreatmentOrthoForest.moment_and_mean_gradient_estimator_func
        super(ContinuousTreatmentOrthoForest, self).__init__(
            nuisance_estimator,
            second_stage_nuisance_estimator,
            parameter_estimator,
            second_stage_parameter_estimator,
            moment_and_mean_gradient_estimator,
            n_trees=n_trees,
            min_leaf_size=min_leaf_size,
            max_depth=max_depth,
            subsample_ratio=subsample_ratio,
            bootstrap=bootstrap,
            n_jobs=n_jobs,
            random_state=random_state)

n_jobs=-1,
                 random_state=None):
        # Copy and/or define models
        self.lambda_reg = lambda_reg
        self.model_T = model_T
        self.model_Y = model_Y
        self.model_T_final = model_T_final
        self.model_Y_final = model_Y_final
        if self.model_T_final is None:
            self.model_T_final = clone(self.model_T, safe=False)
        if self.model_Y_final is None:
            self.model_Y_final = clone(self.model_Y, safe=False)
        # Define nuisance estimators
        nuisance_estimator = ContinuousTreatmentOrthoForest.nuisance_estimator_generator(
            self.model_T, self.model_Y, random_state, second_stage=False)
        second_stage_nuisance_estimator = ContinuousTreatmentOrthoForest.nuisance_estimator_generator(
            self.model_T_final, self.model_Y_final, random_state, second_stage=True)
        # Define parameter estimators
        parameter_estimator = ContinuousTreatmentOrthoForest.parameter_estimator_func
        second_stage_parameter_estimator =\
            ContinuousTreatmentOrthoForest.second_stage_parameter_estimator_gen(self.lambda_reg)
        # Define
        moment_and_mean_gradient_estimator = ContinuousTreatmentOrthoForest.moment_and_mean_gradient_estimator_func
        super(ContinuousTreatmentOrthoForest, self).__init__(
            nuisance_estimator,
            second_stage_nuisance_estimator,
            parameter_estimator,
            second_stage_parameter_estimator,
            moment_and_mean_gradient_estimator,
            n_trees=n_trees,
            min_leaf_size=min_leaf_size,
            max_depth=max_depth,

self.lambda_reg = lambda_reg
        self.model_T = model_T
        self.model_Y = model_Y
        self.model_T_final = model_T_final
        self.model_Y_final = model_Y_final
        if self.model_T_final is None:
            self.model_T_final = clone(self.model_T, safe=False)
        if self.model_Y_final is None:
            self.model_Y_final = clone(self.model_Y, safe=False)
        # Define nuisance estimators
        nuisance_estimator = ContinuousTreatmentOrthoForest.nuisance_estimator_generator(
            self.model_T, self.model_Y, random_state, second_stage=False)
        second_stage_nuisance_estimator = ContinuousTreatmentOrthoForest.nuisance_estimator_generator(
            self.model_T_final, self.model_Y_final, random_state, second_stage=True)
        # Define parameter estimators
        parameter_estimator = ContinuousTreatmentOrthoForest.parameter_estimator_func
        second_stage_parameter_estimator =\
            ContinuousTreatmentOrthoForest.second_stage_parameter_estimator_gen(self.lambda_reg)
        # Define
        moment_and_mean_gradient_estimator = ContinuousTreatmentOrthoForest.moment_and_mean_gradient_estimator_func
        super(ContinuousTreatmentOrthoForest, self).__init__(
            nuisance_estimator,
            second_stage_nuisance_estimator,
            parameter_estimator,
            second_stage_parameter_estimator,
            moment_and_mean_gradient_estimator,
            n_trees=n_trees,
            min_leaf_size=min_leaf_size,
            max_depth=max_depth,
            subsample_ratio=subsample_ratio,
            bootstrap=bootstrap,
            n_jobs=n_jobs,