code: def detection_loss_calculation(
      self, detr_outputs: Dict[str, Tensor], sample_list
  ):
      hs = detr_outputs["hidden_states"]
  
      outputs_class = self.class_embeds[sample_list.dataset_name](hs)
      outputs_coord = self.bbox_embeds[sample_list.dataset_name](
          hs
      ).sigmoid()
      detr_outputs.update(
          {
              "pred_logits": outputs_class[-1],
              "pred_boxes": outputs_coord[-1],
              "hs_for_attr": hs[-1],
          }
      )
      # skip loss computation on test set (which usually doesn't contain labels)
      if sample_list.dataset_type != "test":
          if self.config.base_args.aux_loss:
              detr_outputs["aux_outputs"] = [
                  {"pred_logits": a, "pred_boxes": b, "hs_for_attr": c}
                  for a, b, c in zip(
                      outputs_class[:-1], outputs_coord[:-1], hs[:-1]
                  )
              ]
  
          criterion = self.det_losses[sample_list.dataset_name]
          targets = [
              byte_tensor_to_object(t) for t in sample_list.targets_enc
          ]
          targets = [
              {k: v.to(hs.device) for k, v in t.items()}
              for t in targets
          ]
          sample_list.targets = targets
          loss_dict = criterion(detr_outputs, sample_list.targets)
          weight_dict = criterion.weight_dict
          loss_prefix = (
              f"{sample_list.dataset_type}/{sample_list.dataset_name}/"
          )
          losses = {
              (loss_prefix + f"{k}"): loss_dict[k]
              * weight_dict[k]
              * self.config.detection_loss_weight
              for k in loss_dict.keys()
              if k in weight_dict
          }
          detr_outputs["losses"] = losses
  
      if (
          self.config.heads["detection"][sample_list.dataset_name][
              "use_attr"
          ]
          and self.config.predict_attributes
      ):
          hs_for_attr = detr_outputs["hs_for_attr"]
          top_obj_class = detr_outputs["pred_logits"][..., :-1].argmax(
              dim=-1
          )
          attr_head = self.det_losses[
              sample_list.dataset_name
          ].attribute_head
          detr_outputs["attr_logits"] = attr_head(
              hs_for_attr, top_obj_class
          )
  
      return detr_outputs
  
imports: import logging
  from copy import deepcopy
  from typing import Dict
  import torch
  from mmf.common.registry import registry
  from mmf.models import BaseModel
  from mmf.models.unit.unit_base_model import AttributeHead, build_detection_loss, MLP, UniTBaseModel
  from mmf.modules.encoders import TransformerEncoder
  from mmf.utils.distributed import byte_tensor_to_object
  from torch import nn, Tensor
label_desc: Use image node_id 7 for calling the UniT obj's underlying member method code with example usage: obj.detection_loss_calculation(detr_outputs, sample_list) and returns: detr_outputs