code: def sigmoid_cross_entropy_loss(self, cls_score, labels):
      if cls_score.numel() == 0:
          return cls_score.new_zeros([1])[
              0
          ]  # This is more robust than .sum() * 0.
      B = cls_score.shape[0]
      C = cls_score.shape[1] - 1
  
      target = cls_score.new_zeros(B, C + 1)
      target[range(len(labels)), labels] = 1  # B x (C + 1)
      target = target[:, :C]  # B x C
  
      weight = 1
      if self.use_fed_loss and (
          self.freq_weight is not None
      ):  # fedloss
          appeared = get_fed_loss_inds(
              labels,
              num_sample_cats=self.fed_loss_num_cat,
              C=C,
              weight=self.freq_weight,
          )
          appeared_mask = appeared.new_zeros(C + 1)
          appeared_mask[appeared] = 1  # C + 1
          appeared_mask = appeared_mask[:C]
          fed_w = appeared_mask.view(1, C).expand(B, C)
          weight = weight * fed_w.float()
      # if self.ignore_zero_cats and (self.freq_weight is not None):
      #     w = (self.freq_weight.view(-1) > 1e-4).float()
      #     weight = weight * w.view(1, C).expand(B, C)
      #     # import pdb; pdb.set_trace()
  
      cls_loss = F.binary_cross_entropy_with_logits(
          cls_score[:, :-1], target, reduction="none"
      )  # B x C
      loss = torch.sum(cls_loss * weight) / B
      return loss
  
imports: import json
  from typing import List, Optional
  import torch
  from mmengine.config import ConfigDict
  from mmengine.structures import InstanceData
  from torch import Tensor
  from torch.nn import functional
  from mmdet.models.layers import multiclass_nms
  from mmdet.models.losses import accuracy
  from mmdet.models.roi_heads.bbox_heads import Shared2FCBBoxHead
  from mmdet.models.utils import empty_instances
  from mmdet.registry import MODELS
  from mmdet.structures.bbox import get_box_tensor, scale_boxes
  from mmdet.utils import ConfigType, InstanceList
label_desc: Use image node_id 4 for calling the DeticBBoxHead obj's underlying member method code with example usage: obj.sigmoid_cross_entropy_loss(cls_score, labels) and returns: loss, cls_score