def METHOD_NAME():
    X, y = make_regression(
        n_samples=1000,
        n_features=5,
        n_informative=2,
        n_targets=1,
        random_state=123,
        shuffle=False,
    )
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.3, random_state=123
    )
    svm = SVR(kernel="rbf", gamma="auto")
    svm.fit(X_train, y_train)
    imp_vals, imp_all = feature_importance_permutation(
        predict_method=svm.predict,
        X=X_test,
        y=y_test,
        metric=mean_squared_error,
        num_rounds=1,
        seed=123,
    )
    norm_imp_vals = imp_vals / np.abs(imp_vals).max()
    assert imp_vals.shape == (X_train.shape[1],)
    assert imp_all.shape == (X_train.shape[1], 1)
    assert norm_imp_vals[0] == -1.0