def METHOD_NAME():
    X, y = make_classification(
        n_samples=1000,
        n_features=6,
        n_informative=3,
        n_redundant=0,
        n_repeated=0,
        n_classes=2,
        random_state=0,
        shuffle=False,
    )
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.3, random_state=0, stratify=y
    )
    svm = SVC(C=1.0, kernel="rbf", random_state=0, 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="accuracy",
        num_rounds=1,
        seed=1,
    )
    assert imp_vals.shape == (X_train.shape[1],)
    assert imp_all.shape == (X_train.shape[1], 1)
    assert imp_vals[0] > 0.2
    assert imp_vals[1] > 0.2
    assert imp_vals[2] > 0.2
    assert sum(imp_vals[3:]) <= 0.02