docstr: Perform forward propagation of the detection head and predict
  detection results on the features of the upstream network.
  
  Args:
      x (tuple[Tensor]): Multi-level 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`.
      rescale (bool, optional): Whether to rescale the results.
          Defaults to False.
  
  Returns:
      list[obj:`InstanceData`]: Detection results of each image
      after the post process.
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code: def predict(
      self,
      x: Tuple[Tensor],
      batch_data_samples: SampleList,
      rescale: bool = False,
  ) -> InstanceList:
      """Perform forward propagation of the detection head and predict
      detection results on the features of the upstream network.
  
      Args:
          x (tuple[Tensor]): Multi-level 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`.
          rescale (bool, optional): Whether to rescale the results.
              Defaults to False.
  
      Returns:
          list[obj:`InstanceData`]: Detection results of each image
          after the post process.
      """
      # hard-setting seed to keep results same (if necessary)
      # seed = 0
      # random.seed(seed)
      # torch.manual_seed(seed)
      # torch.cuda.manual_seed_all(seed)
  
      device = x[-1].device
  
      batch_img_metas = [
          data_samples.metainfo for data_samples in batch_data_samples
      ]
  
      (
          time_pairs,
          batch_noise_bboxes,
          batch_noise_bboxes_raw,
          batch_image_size,
      ) = self.prepare_testing_targets(batch_img_metas, device)
  
      predictions = self.predict_by_feat(
          x,
          time_pairs=time_pairs,
          batch_noise_bboxes=batch_noise_bboxes,
          batch_noise_bboxes_raw=batch_noise_bboxes_raw,
          batch_image_size=batch_image_size,
          device=device,
          batch_img_metas=batch_img_metas,
      )
      return predictions
  
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 9 for calling the DynamicDiffusionDetHead obj's underlying member method code with example usage: obj.predict(x, batch_data_samples, rescale) and returns: predictions