code: def _forward_train(self, body_feats, pro_bboxes, pro_feats, targets):
      all_stage_losses = {}
      for stage in range(self.num_stages):
          bbox_head = self.bbox_heads[stage]
          mask_head = self.mask_heads[stage]
  
          roi_feats = self.get_roi_features(
              body_feats, pro_bboxes, self.bbox_roi_extractor
          )
          class_logits, bbox_deltas, pro_feats, attn_feats = bbox_head(
              roi_feats, pro_feats
          )
          bbox_pred = self.bbox_heads[stage].refine_bboxes(
              pro_bboxes, bbox_deltas
          )
  
          indices = self.loss_helper.matcher(
              {
                  "pred_logits": class_logits.detach(),
                  "pred_boxes": bbox_pred.detach(),
              },
              targets,
          )
          avg_factor = paddle.to_tensor(
              [sum(len(tgt["labels"]) for tgt in targets)],
              dtype="float32",
          )
          if paddle.distributed.get_world_size() > 1:
              paddle.distributed.all_reduce(avg_factor)
              avg_factor /= paddle.distributed.get_world_size()
          avg_factor = paddle.clip(avg_factor, min=1.0)
  
          loss_classes = self.loss_helper.loss_classes(
              class_logits, targets, indices, avg_factor
          )
          if sum(len(v["labels"]) for v in targets) == 0:
              loss_bboxes = {
                  "loss_bbox": paddle.to_tensor([0.0]),
                  "loss_giou": paddle.to_tensor([0.0]),
              }
              loss_masks = {"loss_mask": paddle.to_tensor([0.0])}
          else:
              loss_bboxes = self.loss_helper.loss_bboxes(
                  bbox_pred, targets, indices, avg_factor
              )
  
              pos_attn_feats = paddle.concat(
                  [
                      paddle.gather(src, src_idx, axis=0)
                      for src, (src_idx, _) in zip(attn_feats, indices)
                  ]
              )
              pos_bbox_pred = [
                  paddle.gather(src, src_idx, axis=0)
                  for src, (src_idx, _) in zip(
                      bbox_pred.detach(), indices
                  )
              ]
              pos_roi_feats = self.get_roi_features(
                  body_feats, pos_bbox_pred, self.mask_roi_extractor
              )
              mask_logits = mask_head(pos_roi_feats, pos_attn_feats)
              loss_masks = self.loss_helper.loss_masks(
                  pos_bbox_pred,
                  mask_logits,
                  targets,
                  indices,
                  avg_factor,
              )
  
          for loss in [loss_classes, loss_bboxes, loss_masks]:
              for key in loss.keys():
                  all_stage_losses[f"stage{stage}_{key}"] = loss[key]
  
          pro_bboxes = bbox_pred.detach()
  
      return all_stage_losses
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import paddle
  from paddle import nn
  from ppdet.core.workspace import register
  from ppdet.modeling import initializer
  from .roi_extractor import RoIAlign
  from ..bbox_utils import delta2bbox_v2
  from ..cls_utils import _get_class_default_kwargs
  from ..layers import MultiHeadAttention
label_desc: Use image node_id 4 for calling the SparseRoIHead obj's underlying member method code with example usage: obj._forward_train(body_feats, pro_bboxes, pro_feats, targets) and returns: all_stage_losses