def METHOD_NAME(self, img, img_metas, rescale=False):
    """Test function without test time augmentation.
    Args:
        imgs (list[torch.Tensor]): List of multiple images
        img_metas (list[dict]): List of image information.
        rescale (bool, optional): Whether to rescale the results.
            Defaults to False.
    Returns:
        list[list[np.ndarray]]: BBox results of each image and classes.
            The outer list corresponds to each image. The inner list
            corresponds to each class.
    """
    x = self.extract_feat(img)
    outs = self.bbox_head(x)
    if torch.onnx.is_in_onnx_export():
        print('single_stage.py in-onnx-export')
        print(outs.__class__)
        cls_score, bbox_pred = outs
        for c in cls_score:
            print(c.shape)
        for c in bbox_pred:
            print(c.shape)
        return outs
    bbox_list = self.bbox_head.get_bboxes(
        *outs, img_metas, rescale=rescale)
    # skip post-processing when exporting to ONNX
    if torch.onnx.is_in_onnx_export():
        return bbox_list
    bbox_results = [
        bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes)
        for det_bboxes, det_labels in bbox_list
    ]
    return bbox_results