def METHOD_NAME(self, batch, outputs: Optional[Any] = None):
    """
    Args:
        batch (dict): a eval batch of the format:
        ``img`` (List[torch.Tensor]): list of image torch.Tensors of shape (batch, c, h , w).
        ``img_metas`` (List[Dict]): (1, batch_size) list of ``image_meta`` dicts.
    Returns: model predictions: A batch_size length list of dictionaries containg detection boxes in (x,y, x2, y2) format, class labels, and class probabilities.
    """
    device = batch['img'][0].device
    batch.pop('gt_labels')
    batch.pop('gt_bboxes')
    results = self.model(return_loss=False, rescale=True, **batch)  # models behave differently in eval mode
    # outputs are a list of bbox results (x, y, x2, y2, score)
    # pack mmdet bounding boxes and labels into the format for torchmetrics MAP expects
    preds = []
    for bbox_result in results:
        boxes_scores = np.vstack(bbox_result)
        boxes, scores = torch.from_numpy(boxes_scores[..., :-1]).to(device), torch.from_numpy(
            boxes_scores[..., -1]).to(device)
        labels = [np.full(result.shape[0], i, dtype=np.int32) for i, result in enumerate(bbox_result)]
        pred = {
            'labels': torch.from_numpy(np.concatenate(labels)).to(device).long(),
            'boxes': boxes.float(),
            'scores': scores.float()
        }
        preds.append(pred)
    return preds