def METHOD_NAME():
    np.random.seed(1)
    n_classes = 3
    chunk = [i for i in range(50)]
    population_size = 10_000
    y_true = np.random.randint(0, n_classes, population_size)
    y_true[0 : population_size * 2 // 3] = 0
    lb = LabelBinarizer()
    y_true = lb.fit_transform(y_true)
    y_pred_proba = abs(y_true - 0.49) + np.random.normal(0, 0.01, np.shape(y_true))
    y_pred_proba = (y_pred_proba.T / (y_pred_proba.sum(axis=1))).T
    list_of_y_pred_probas = list(y_pred_proba.T)
    list_of_y_trues = list(y_true.T)
    components = mse.auroc_sampling_error_components(list_of_y_trues, list_of_y_pred_probas)
    sampling_error = mse.auroc_sampling_error(components, chunk)
    assert np.round(sampling_error, 4) == 0.0237