def METHOD_NAME(self,
              x: Tuple[Tensor],
              sampling_results: List[SamplingResult],
              neg_label_weights: List[Tensor] = None) -> dict:
    """Perform forward propagation and loss calculation of the bbox head on
    the features of the upstream network.
    Args:
        x (tuple[Tensor]): List of multi-level img features.
        sampling_results (list["obj:`SamplingResult`]): Sampling results.
    Returns:
        dict[str, Tensor]: Usually returns a dictionary with keys:
        - `cls_score` (Tensor): Classification scores.
        - `bbox_pred` (Tensor): Box energies / deltas.
        - `bbox_feats` (Tensor): Extract bbox RoI features.
        - `loss_bbox` (dict): A dictionary of bbox loss components.
    """
    rois = bbox2roi([res.priors for res in sampling_results])
    bbox_results = self._bbox_forward(x, rois)
    bbox_targets = self.bbox_head.get_targets(sampling_results,
                                              self.train_cfg)
    # neg_label_weights obtained by sampler is image-wise, mapping back to
    # the corresponding location in label weights
    if neg_label_weights[0] is not None:
        label_weights = bbox_targets[1]
        cur_num_rois = 0
        for i in range(len(sampling_results)):
            num_pos = sampling_results[i].pos_inds.size(0)
            num_neg = sampling_results[i].neg_inds.size(0)
            label_weights[cur_num_rois + num_pos:cur_num_rois + num_pos +
                          num_neg] = neg_label_weights[i]
            cur_num_rois += num_pos + num_neg
    cls_score = bbox_results['cls_score']
    bbox_pred = bbox_results['bbox_pred']
    # Apply ISR-P
    isr_cfg = self.train_cfg.get('isr', None)
    if isr_cfg is not None:
        bbox_targets = isr_p(
            cls_score,
            bbox_pred,
            bbox_targets,
            rois,
            sampling_results,
            self.bbox_head.loss_cls,
            self.bbox_head.bbox_coder,
            **isr_cfg,
            num_class=self.bbox_head.num_classes)
    loss_bbox = self.bbox_head.loss(cls_score, bbox_pred, rois,
                                    *bbox_targets)
    # Add CARL Loss
    carl_cfg = self.train_cfg.get('carl', None)
    if carl_cfg is not None:
        loss_carl = carl_loss(
            cls_score,
            bbox_targets[0],
            bbox_pred,
            bbox_targets[2],
            self.bbox_head.loss_bbox,
            **carl_cfg,
            num_class=self.bbox_head.num_classes)
        loss_bbox.update(loss_carl)
    bbox_results.update(loss_bbox=loss_bbox)
    return bbox_results