code: def prepare_training_targets(self, batch_data_samples):
      # hard-setting seed to keep results same (if necessary)
      # random.seed(0)
      # torch.manual_seed(0)
      # torch.cuda.manual_seed_all(0)
      # torch.backends.cudnn.deterministic = True
      # torch.backends.cudnn.benchmark = False
  
      batch_gt_instances = []
      batch_pred_instances = []
      batch_gt_instances_ignore = []
      batch_img_metas = []
      for data_sample in batch_data_samples:
          img_meta = data_sample.metainfo
          gt_instances = data_sample.gt_instances
  
          gt_bboxes = gt_instances.bboxes
          h, w = img_meta["img_shape"]
          image_size = gt_bboxes.new_tensor([w, h, w, h])
  
          norm_gt_bboxes = gt_bboxes / image_size
          norm_gt_bboxes_cxcywh = bbox_xyxy_to_cxcywh(norm_gt_bboxes)
          pred_instances = self.prepare_diffusion(
              norm_gt_bboxes_cxcywh, image_size
          )
  
          gt_instances.set_metainfo(dict(image_size=image_size))
          gt_instances.norm_bboxes_cxcywh = norm_gt_bboxes_cxcywh
  
          batch_gt_instances.append(gt_instances)
          batch_pred_instances.append(pred_instances)
          batch_img_metas.append(data_sample.metainfo)
          if "ignored_instances" in data_sample:
              batch_gt_instances_ignore.append(
                  data_sample.ignored_instances
              )
          else:
              batch_gt_instances_ignore.append(None)
      return (
          batch_gt_instances,
          batch_pred_instances,
          batch_gt_instances_ignore,
          batch_img_metas,
      )
  
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 6 for calling the DynamicDiffusionDetHead obj's underlying member method code with example usage: obj.prepare_training_targets(batch_data_samples) and returns: batch_gt_instances,  batch_pred_instances,  batch_gt_instances_ignore,  batch_img_metas