def METHOD_NAME(self):
    sampler = _utils.BalancedPositiveNegativeSampler(4, 0.25)
    # keep all 6 negatives first, then add 3 positives, last two are ignore
    matched_idxs = [torch.tensor([0, 0, 0, 0, 0, 0, 1, 1, 1, -1, -1])]
    pos, neg = sampler(matched_idxs)
    # we know the number of elements that should be sampled for the positive (1)
    # and the negative (3), and their location. Let's make sure that they are
    # there
    assert pos[0].sum() == 1
    assert pos[0][6:9].sum() == 1
    assert neg[0].sum() == 3
    assert neg[0][0:6].sum() == 3