def METHOD_NAME():
    size = 100
    np_y_pred = np.random.rand(size, 1)
    np_y = np.zeros((size,))
    np_y[size // 2 :] = 1
    sk_fpr, sk_tpr, sk_thresholds = roc_curve(np_y, np_y_pred)
    roc_curve_metric = RocCurve()
    y_pred = torch.from_numpy(np_y_pred)
    y = torch.from_numpy(np_y)
    roc_curve_metric.update((y_pred, y))
    fpr, tpr, thresholds = roc_curve_metric.compute()
    assert np.array_equal(fpr, sk_fpr)
    assert np.array_equal(tpr, sk_tpr)
    # assert thresholds almost equal, due to numpy->torch->numpy conversion
    np.testing.assert_array_almost_equal(thresholds, sk_thresholds)