def METHOD_NAME(exact_shap_regression_dataset):
    X_train, _, y_train, _ = exact_shap_regression_dataset
    # Train arbitrary model to get some coefficients
    mod = cuml.LinearRegression().fit(X_train, y_train)
    # Single background and foreground instance
    # Gives zero effect to features when they are 'off'
    # and the effect of the regression coefficient when they are 'on'
    X_background = np.zeros((1, X_train.shape[1]))
    X_foreground = np.ones((1, X_train.shape[1]))
    explainer = PermutationExplainer(model=mod.predict, data=X_background)
    shap_values = explainer.shap_values(
        X_foreground,
        npermutations=5,
    )
    assert np.allclose(mod.coef_, shap_values, rtol=1e-04, atol=1e-04)