How to use the econml.tests.test_statsmodels._summarize function in econml

X = np.random.binomial(1, .8, size=(n, d))
                                T = np.hstack([np.random.binomial(1, .5 * X[:, 0] + .25,
                                                                  size=(n,)).reshape(-1, 1) for _ in range(d_t)])
                                true_coef = np.hstack([np.hstack([it + np.arange(p).reshape(-1, 1),
                                                                  it + np.ones((p, 1)), np.zeros((p, d_x - 1))])
                                                       for it in range(d_t)])

                                def true_effect(x, t):
                                    return cross_product(
                                        np.hstack([np.ones((x.shape[0], 1)), x[:, :d_x]]), t) @ true_coef.T
                                y = true_effect(X, T) + X[:, [0] * p] +\
                                    (hetero_coef * X[:, [0]] + 1) * np.random.normal(0, 1, size=(n, p))

                                XT = np.hstack([X, T])
                                X1, X2, y1, y2, X_final_first, X_final_sec, y_sum_first, y_sum_sec,\
                                    n_sum_first, n_sum_sec, var_first, var_sec = _summarize(XT, y)
                                X = np.vstack([X1, X2])
                                y = np.concatenate((y1, y2))
                                X_final = np.vstack([X_final_first, X_final_sec])
                                y_sum = np.concatenate((y_sum_first, y_sum_sec))
                                n_sum = np.concatenate((n_sum_first, n_sum_sec))
                                var_sum = np.concatenate((var_first, var_sec))
                                first_half_sum = len(y_sum_first)
                                first_half = len(y1)
                                for cov_type in cov_type_list:
                                    class SplitterSum:
                                        def __init__(self):
                                            return

                                        def split(self, X, T):
                                            return [(np.arange(0, first_half_sum),
                                                     np.arange(first_half_sum, X.shape[0])),