def METHOD_NAME(self, input: Dict[str, Any]) -> Dict[str, Any]:
    scores = []
    labels = []
    masks = []
    boxes = []
    track_ids = []
    for ii in tqdm(range(len(input['iids']))):
        img = input['imgs'][ii]
        img_meta = input['img_metas'][ii]
        iid = input['iids'][ii]
        x = np.transpose(img, [2, 0, 1])
        x = np.expand_dims(x, 0)
        x = torch.from_numpy(x).to(self.device)
        with torch.no_grad():
            segm_results = self.model(x, img_meta, rescale=True, iid=iid)
        _, _, _, vis_sem, vis_tracker, label, binary_mask, track_id, thing_bbox_for_tracking = segm_results
        scores.append([0.99] * len(label))
        labels.append(label)
        masks.append(binary_mask)
        boxes.append(thing_bbox_for_tracking)
        track_ids.append(track_id)
    output = {
        'scores': scores,
        'labels': labels,
        'masks': masks,
        'boxes': boxes,
        'uuid': track_ids
    }
    return output