def METHOD_NAME(self,
                  img,
                  img_metas,
                  gt_bboxes,
                  gt_labels,
                  gt_keypointss=None,
                  gt_bboxes_ignore=None):
    """
    Args:
        img (Tensor): Input images of shape (N, C, H, W).
            Typically these should be mean centered and std scaled.
        img_metas (list[dict]): A List of image info dict where each dict
            has: 'img_shape', 'scale_factor', 'flip', and may also contain
            'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
            For details on the values of these keys see
            :class:`mmdet.datasets.pipelines.Collect`.
        gt_bboxes (list[Tensor]): Each item are the truth boxes for each
            image in [tl_x, tl_y, br_x, br_y] format.
        gt_labels (list[Tensor]): Class indices corresponding to each box
        gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
            boxes can be ignored when computing the loss.
    Returns:
        dict[str, Tensor]: A dictionary of loss components.
    """
    super(SingleStageDetector, self).METHOD_NAME(img, img_metas)
    x = self.extract_feat(img)
    losses = self.bbox_head.METHOD_NAME(x, img_metas, gt_bboxes,
                                          gt_labels, gt_keypointss,
                                          gt_bboxes_ignore)
    return losses