def METHOD_NAME(self, xgb_params_01, xgb_params_02,
                              xgb_params_03):
    from sklearn.datasets import load_digits
    from sklearn.metrics import mean_squared_error
    digits_2class = load_digits(n_class=2)
    digits_5class = load_digits(n_class=5)
    X_2class = digits_2class['data']
    y_2class = digits_2class['target']
    X_5class = digits_5class['data']
    y_5class = digits_5class['target']
    dtrain_2class = xgb.DMatrix(X_2class, label=y_2class)
    dtrain_5class = xgb.DMatrix(X_5class, label=y_5class)
    gbdt_01 = xgb.train(xgb_params_01, dtrain_2class,
                        num_boost_round=10)
    ntrees_01 = len(gbdt_01.get_dump())
    assert ntrees_01 == 10
    gbdt_02 = xgb.train(xgb_params_01, dtrain_2class,
                        num_boost_round=0)
    gbdt_02.save_model('xgb_tc.json')
    gbdt_02a = xgb.train(xgb_params_01, dtrain_2class,
                         num_boost_round=10, xgb_model=gbdt_02)
    gbdt_02b = xgb.train(xgb_params_01, dtrain_2class,
                         num_boost_round=10, xgb_model="xgb_tc.json")
    ntrees_02a = len(gbdt_02a.get_dump())
    ntrees_02b = len(gbdt_02b.get_dump())
    assert ntrees_02a == 10
    assert ntrees_02b == 10
    res1 = mean_squared_error(y_2class, gbdt_01.predict(dtrain_2class))
    res2 = mean_squared_error(y_2class, gbdt_02a.predict(dtrain_2class))
    assert res1 == res2
    res1 = mean_squared_error(y_2class, gbdt_01.predict(dtrain_2class))
    res2 = mean_squared_error(y_2class, gbdt_02b.predict(dtrain_2class))
    assert res1 == res2
    gbdt_03 = xgb.train(xgb_params_01, dtrain_2class,
                        num_boost_round=3)
    gbdt_03.save_model('xgb_tc.json')
    gbdt_03a = xgb.train(xgb_params_01, dtrain_2class,
                         num_boost_round=7, xgb_model=gbdt_03)
    gbdt_03b = xgb.train(xgb_params_01, dtrain_2class,
                         num_boost_round=7, xgb_model="xgb_tc.json")
    ntrees_03a = len(gbdt_03a.get_dump())
    ntrees_03b = len(gbdt_03b.get_dump())
    assert ntrees_03a == 10
    assert ntrees_03b == 10
    os.remove('xgb_tc.json')
    res1 = mean_squared_error(y_2class, gbdt_03a.predict(dtrain_2class))
    res2 = mean_squared_error(y_2class, gbdt_03b.predict(dtrain_2class))
    assert res1 == res2
    gbdt_04 = xgb.train(xgb_params_02, dtrain_2class, num_boost_round=3)
    res1 = mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class))
    res2 = mean_squared_error(
        y_2class,
        gbdt_04.predict(
            dtrain_2class, iteration_range=(0, gbdt_04.num_boosted_rounds())
        ),
    )
    assert res1 == res2
    gbdt_04 = xgb.train(
        xgb_params_02, dtrain_2class, num_boost_round=7, xgb_model=gbdt_04
    )
    res1 = mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class))
    res2 = mean_squared_error(
        y_2class,
        gbdt_04.predict(
            dtrain_2class, iteration_range=(0, gbdt_04.num_boosted_rounds())
        )
    )
    assert res1 == res2
    gbdt_05 = xgb.train(xgb_params_03, dtrain_5class,
                        num_boost_round=7)
    gbdt_05 = xgb.train(xgb_params_03,
                        dtrain_5class,
                        num_boost_round=3,
                        xgb_model=gbdt_05)
    res1 = gbdt_05.predict(dtrain_5class)
    res2 = gbdt_05.predict(
        dtrain_5class, iteration_range=(0, gbdt_05.num_boosted_rounds())
    )
    np.testing.assert_almost_equal(res1, res2)