code: def forward(self, features, init_bboxes, init_t, init_features=None):
      time = self.time_mlp(
          init_t,
      )
  
      inter_class_logits = []
      inter_pred_bboxes = []
  
      bs = len(features[0])
      bboxes = init_bboxes
  
      if init_features is not None:
          init_features = init_features[None].repeat(1, bs, 1)
          proposal_features = init_features.clone()
      else:
          proposal_features = None
  
      for head_idx, single_head in enumerate(self.head_series):
          class_logits, pred_bboxes, proposal_features = single_head(
              features,
              bboxes,
              proposal_features,
              self.roi_extractor,
              time,
          )
          if self.deep_supervision:
              inter_class_logits.append(class_logits)
              inter_pred_bboxes.append(pred_bboxes)
          bboxes = pred_bboxes.detach()
  
      if self.deep_supervision:
          return torch.stack(inter_class_logits), torch.stack(
              inter_pred_bboxes
          )
      else:
          return class_logits[None, ...], pred_bboxes[None, ...]
  
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 4 for calling the DynamicDiffusionDetHead obj's underlying member method code with example usage: obj.forward(features, init_bboxes, init_t, init_features) and returns: torch,  torch, class_logits,  pred_bboxes