def METHOD_NAME(x, y, split_number, split_size, k, seed=None):
    """
    Parameters
    ----------
    x : ht.DNDarray
        array containing data vectors
    y : ht.DNDarray
        array containing the labels for x (must be in same order)
    split_number: int
        the number of test iterations
    split_size : int
        the number of vectors used by the KNeighborsClassifier-Algorithm
    k : int
        The number of neighbours for KNeighborsClassifier-Algorithm
    seed : int
        Seed for the random generator used in creating folds. Used for deterministic testing purposes.
    Returns
    -------
    accuracies : ht.DNDarray
        array of shape (split_number,) containing the accuracy per run
    """
    assert len(x) == len(y)
    assert split_size < len(x)
    assert k < len(x)
    accuracies = []
    for split_index in range(split_number):
        fold_x, fold_y, verification_x, verification_y = create_fold(x, y, split_size, seed)
        classifier = KNeighborsClassifier(k)
        classifier.fit(fold_x, fold_y)
        result_y = classifier.predict(verification_x)
        accuracies.append(calculate_accuracy(result_y, verification_y).item())
    return accuracies