docstr: Perform forward propagation and loss calculation of the detection
  head on the features of the upstream network.
  
  Args:
      x (tuple[Tensor]): Features from the upstream network, each is
          a 4D-tensor.
      batch_data_samples (List[:obj:`DetDataSample`]): The Data
          Samples. It usually includes information such as
          `gt_instance`, `gt_panoptic_seg` and `gt_sem_seg`.
  
  Returns:
      dict: A dictionary of loss components.
docstr_tok: ["Perform","forward","propagation","and","loss","calculation","of","the","detection","head","on","the","features","of","the","upstream","network",".","Args",":","x","(","tuple","[","Tensor","]",")",":","Features","from","the","upstream","network",",","each","is","a","4D-tensor",".","batch_data_samples","(","List","[",":","obj",":","`","DetDataSample","`","]",")",":","The","Data","Samples",".","It","usually","includes","information","such","as","`","gt_instance","`",",","`","gt_panoptic_seg","`","and","`","gt_sem_seg","`",".","Returns",":","dict",":","A","dictionary","of","loss","components","."]
code: def loss(
      self, x: Tuple[Tensor], batch_data_samples: SampleList
  ) -> dict:
      """Perform forward propagation and loss calculation of the detection
      head on the features of the upstream network.
  
      Args:
          x (tuple[Tensor]): Features from the upstream network, each is
              a 4D-tensor.
          batch_data_samples (List[:obj:`DetDataSample`]): The Data
              Samples. It usually includes information such as
              `gt_instance`, `gt_panoptic_seg` and `gt_sem_seg`.
  
      Returns:
          dict: A dictionary of loss components.
      """
      prepare_outputs = self.prepare_training_targets(
          batch_data_samples
      )
      (
          batch_gt_instances,
          batch_pred_instances,
          batch_gt_instances_ignore,
          batch_img_metas,
      ) = prepare_outputs
  
      batch_diff_bboxes = torch.stack(
          [
              pred_instances.diff_bboxes_abs
              for pred_instances in batch_pred_instances
          ]
      )
      batch_time = torch.stack(
          [
              pred_instances.time
              for pred_instances in batch_pred_instances
          ]
      )
  
      pred_logits, pred_bboxes = self(x, batch_diff_bboxes, batch_time)
  
      output = {
          "pred_logits": pred_logits[-1],
          "pred_boxes": pred_bboxes[-1],
      }
      if self.deep_supervision:
          output["aux_outputs"] = [
              {"pred_logits": a, "pred_boxes": b}
              for a, b in zip(pred_logits[:-1], pred_bboxes[:-1])
          ]
  
      losses = self.criterion(
          output, batch_gt_instances, batch_img_metas
      )
      return losses
  
imports: import copy
  import math
  import random
  import warnings
  from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import build_activation_layer
  from mmcv.ops import batched_nms
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.structures import SampleList
  from mmdet.structures.bbox import bbox2roi, bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh, get_box_wh, scale_boxes
  from mmdet.utils import InstanceList
label_desc: Use image node_id 5 for calling the DynamicDiffusionDetHead obj's underlying member method code with example usage: obj.loss(x, batch_data_samples) and returns: losses