def METHOD_NAME(self):
    s = 256
    img_metas = {
        'img_shape': (s, s, 3),
        'ori_shape': (s, s, 3),
        'scale_factor': 1,
        'batch_input_shape': (s, s, 3)
    }
    mask_head = DecoupledSOLOHead(num_classes=4, in_channels=1)
    cls_scores = torch.empty(0, 80)
    mask_preds_x = torch.empty(0, 16, 16)
    mask_preds_y = torch.empty(0, 16, 16)
    test_cfg = ConfigDict(
        score_thr=0.1,
        mask_thr=0.5,
    )
    results = mask_head._predict_by_feat_single(
        cls_scores=cls_scores,
        mask_preds_x=mask_preds_x,
        mask_preds_y=mask_preds_y,
        img_meta=img_metas,
        cfg=test_cfg)
    self.assertIsInstance(results, InstanceData)
    self.assertEqual(len(results), 0)