def METHOD_NAME():
    self = MaxIoUAssigner(
        pos_iou_thr=0.5,
        neg_iou_thr=0.5,
        ignore_iof_thr=0.5,
        ignore_wrt_candidates=False,
    )
    priors = torch.FloatTensor([
        [0, 0, 10, 10],
        [10, 10, 20, 20],
        [5, 5, 15, 15],
        [30, 32, 40, 42],
    ])
    gt_bboxes = torch.FloatTensor([
        [0, 0, 10, 9],
        [0, 10, 10, 19],
    ])
    gt_labels = torch.LongTensor([2, 3])
    gt_bboxes_ignore = torch.Tensor([
        [30, 30, 40, 40],
    ])
    pred_instances = InstanceData(priors=priors)
    gt_instances = InstanceData(bboxes=gt_bboxes, labels=gt_labels)
    gt_instances_ignore = InstanceData(bboxes=gt_bboxes_ignore)
    assign_result = self.assign(
        pred_instances, gt_instances, gt_instances_ignore=gt_instances_ignore)
    expected_gt_inds = torch.LongTensor([1, 0, 2, -1])
    assert torch.all(assign_result.gt_inds == expected_gt_inds)