How to use the susi.SOMRegressor function in susi

def test_mexicanhat_nbh_dist_weight_mode():
    som = susi.SOMRegressor(nbh_dist_weight_mode="mexican-hat")
    som.fit(X_train, y_train)
    som.predict(X_test)
    with pytest.raises(Exception):
        som = susi.SOMRegressor(nbh_dist_weight_mode="pseudogaussian")
        som.fit(X_train, y_train)

def test_mexicanhat_nbh_dist_weight_mode():
    som = susi.SOMRegressor(nbh_dist_weight_mode="mexican-hat")
    som.fit(X_train, y_train)
    som.predict(X_test)
    with pytest.raises(Exception):
        som = susi.SOMRegressor(nbh_dist_weight_mode="pseudogaussian")
        som.fit(X_train, y_train)

def test_som_regressor_init(n_rows, n_columns):
    som_reg = susi.SOMRegressor(
        n_rows=n_rows, n_columns=n_columns)
    assert(som_reg.n_rows == n_rows)
    assert(som_reg.n_columns == n_columns)

def test_MultiOutputRegressor():
    mor = MultiOutputRegressor(
        estimator=susi.SOMRegressor(n_jobs=2),
        n_jobs=2
    )
    mor.fit(X, y)

def test_get_estimation_map(super_som):
    som = susi.SOMRegressor()  # works the same with SOMClassifier
    som.super_som_ = super_som
    assert np.array_equal(som.get_estimation_map(), super_som)

def test_init_super_som_regressor(X, y, init_mode):
    som = susi.SOMRegressor(init_mode_supervised=init_mode)
    som.X_ = X
    som.y_ = y
    som.labeled_indices_ = np.where(som.y_ != -1)[0]
    som.init_super_som()

    # test type
    assert isinstance(som.super_som_, np.ndarray)

    # test shape
    n_rows = som.n_rows
    n_columns = som.n_columns
    assert som.super_som_.shape == (n_rows, n_columns, som.n_regression_vars_)

    with pytest.raises(Exception):
        som = susi.SOMRegressor(init_mode_supervised="pca")
        som.X_ = X

som = susi.SOMRegressor(init_mode_supervised=init_mode)
    som.X_ = X
    som.y_ = y
    som.labeled_indices_ = np.where(som.y_ != -1)[0]
    som.init_super_som()

    # test type
    assert isinstance(som.super_som_, np.ndarray)

    # test shape
    n_rows = som.n_rows
    n_columns = som.n_columns
    assert som.super_som_.shape == (n_rows, n_columns, som.n_regression_vars_)

    with pytest.raises(Exception):
        som = susi.SOMRegressor(init_mode_supervised="pca")
        som.X_ = X
        som.y_ = y
        som.init_super_som()

def test_calc_estimation_output(n_rows, n_columns, unsuper_som, super_som,
                                datapoint, expected):
    som = susi.SOMRegressor(n_rows=n_rows, n_columns=n_columns)
    som.unsuper_som_ = unsuper_som
    som.super_som_ = super_som
    output = som.calc_estimation_output(datapoint)
    print(output)
    assert np.array_equal(output, expected)

    with pytest.raises(ValueError):
        som.calc_estimation_output(datapoint, mode="wrong")