code: def do_results_post_process(results_list, cfg, batch_img_metas=None):
      processed_results = []
      for results, img_meta in zip(results_list, batch_img_metas):
          assert img_meta.get("scale_factor") is not None
          scale_factor = [1 / s for s in img_meta["scale_factor"]]
          results.bboxes = scale_boxes(results.bboxes, scale_factor)
          # clip w, h
          h, w = img_meta["ori_shape"]
          results.bboxes[:, 0::2] = results.bboxes[:, 0::2].clamp(
              min=0, max=w
          )
          results.bboxes[:, 1::2] = results.bboxes[:, 1::2].clamp(
              min=0, max=h
          )
  
          # filter small size bboxes
          if cfg.get("min_bbox_size", 0) >= 0:
              w, h = get_box_wh(results.bboxes)
              valid_mask = (w > cfg.min_bbox_size) & (
                  h > cfg.min_bbox_size
              )
              if not valid_mask.all():
                  results = results[valid_mask]
          processed_results.append(results)
  
      return processed_results
  
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 11 for calling the DynamicDiffusionDetHead obj's underlying member method code with example usage: obj.do_results_post_process(results_list, cfg, batch_img_metas) and returns: processed_results