How to use the mlxtend._base._BaseEstimator function in mlxtend

def test_predict_2():
    X = np.array([[1], [2], [3]])
    est = _BaseEstimator(print_progress=0, random_seed=1)

    est.fit(X)
    est.predict(X)

def test_init():
    est = _BaseEstimator(print_progress=0, random_seed=1)
    assert hasattr(est, 'print_progress')
    assert hasattr(est, 'random_seed')

def test_minibatches_divisible():
    ary = np.array([1, 2, 3, 4, 5, 6, 7, 8])
    est = _BaseEstimator(print_progress=0, random_seed=1)
    gen_arys = est._yield_minibatches_idx(n_batches=2, data_ary=ary)
    arys = list(gen_arys)

    assert (arys[0] == np.array([7, 2, 1, 6])).all()
    assert (arys[1] == np.array([0, 4, 3, 5])).all()

def test_check_array_2():
    X = list([[1], [2], [3]])
    est = _BaseEstimator(print_progress=0, random_seed=1)

    assert_raises(ValueError,
                  'X must be a numpy array',
                  est._check_arrays,
                  X)

def test_init_params():
    est = _BaseEstimator(print_progress=0, random_seed=1)
    b, w = est._init_params(weights_shape=(3, 3),
                            bias_shape=(1,),
                            random_seed=0)
    assert b == np.array([0.0]), b

    expect_w = np.array([[0.016, -0.006, -0.005],
                         [-0.011, 0.009, -0.023],
                         [0.017, -0.008, 0.003]])
    np.testing.assert_almost_equal(w, expect_w, decimal=3)

def test_predict_1():
    X = np.array([[1], [2], [3]])
    est = _BaseEstimator(print_progress=0, random_seed=1)

    assert_raises(AttributeError,
                  'Model is not fitted, yet.',
                  est.predict,
                  X)

def test_check_array_1():
    X = np.array([1, 2, 3])
    est = _BaseEstimator(print_progress=0, random_seed=1)
    assert_raises(ValueError,
                  'X must be a 2D array. Try X[:, numpy.newaxis]',
                  est._check_arrays,
                  X)

def test_minibatch_1sample():
    ary = np.array([1, 2, 3, 4, 5, 6, 7])
    est = _BaseEstimator(print_progress=0, random_seed=1)
    gen_arys = est._yield_minibatches_idx(n_batches=7, data_ary=ary)
    arys = list(gen_arys)

    assert len(arys) == 7
    assert arys[0] == np.array([6])

def test_shuffle():
    X = np.array([[1], [2], [3]])
    y = np.array([1, 2, 3])
    est = _BaseEstimator(print_progress=0, random_seed=1)
    X_sh, y_sh = est._shuffle_arrays(arrays=[X, np.array(y)])
    np.testing.assert_equal(X_sh, np.array([[1], [3], [2]]))
    np.testing.assert_equal(y_sh, np.array([1, 3, 2]))

def test_check_array_3():
    X = np.array([[1], [2], [3]])
    est = _BaseEstimator(print_progress=0, random_seed=1)
    est._check_arrays(X)