def METHOD_NAME(self):
    proposals = [torch.randint(-50, 50, (20, 4), dtype=torch.float32)]
    gt_boxes = [torch.zeros((0, 4), dtype=torch.float32)]
    gt_labels = [torch.tensor([[0]], dtype=torch.int64)]
    box_roi_pool = MultiScaleRoIAlign(featmap_names=["0", "1", "2", "3"], output_size=7, sampling_ratio=2)
    resolution = box_roi_pool.output_size[0]
    representation_size = 1024
    box_head = TwoMLPHead(4 * resolution**2, representation_size)
    representation_size = 1024
    box_predictor = FastRCNNPredictor(representation_size, 2)
    roi_heads = RoIHeads(
        # Box
        box_roi_pool,
        box_head,
        box_predictor,
        0.5,
        0.5,
        512,
        0.25,
        None,
        0.05,
        0.5,
        100,
    )
    matched_idxs, labels = roi_heads.assign_targets_to_proposals(proposals, gt_boxes, gt_labels)
    assert matched_idxs[0].sum() == 0
    assert matched_idxs[0].shape == torch.Size([proposals[0].shape[0]])
    assert matched_idxs[0].dtype == torch.int64
    assert labels[0].sum() == 0
    assert labels[0].shape == torch.Size([proposals[0].shape[0]])
    assert labels[0].dtype == torch.int64