class_docstr: Stage of CascadeRPNHead.
  
  Args:
      in_channels (int): Number of channels in the input feature map.
      anchor_generator (:obj:`ConfigDict` or dict): anchor generator config.
      adapt_cfg (:obj:`ConfigDict` or dict): adaptation config.
      bridged_feature (bool): whether update rpn feature. Defaults to False.
      with_cls (bool): whether use classification branch. Defaults to True.
      init_cfg :obj:`ConfigDict` or list[:obj:`ConfigDict`] or dict or
          list[dict], optional): Initialization config dict.
          Defaults to None.
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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.
docstr_tok: ["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","."]
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.
      """
      batch_img_metas = [
          data_samples.metainfo for data_samples in batch_data_samples
      ]
  
      featmap_sizes = [featmap.size()[-2:] for featmap in x]
      device = x[0].device
      anchor_list, _ = self.get_anchors(
          featmap_sizes, batch_img_metas, device=device
      )
  
      if self.adapt_cfg["type"] == "offset":
          offset_list = self.anchor_offset(
              anchor_list, self.anchor_strides, featmap_sizes
          )
      else:
          offset_list = None
  
      x, cls_score, bbox_pred = self(x, offset_list)
      predictions = self.stages[-1].predict_by_feat(
          anchor_list,
          cls_score,
          bbox_pred,
          batch_img_metas=batch_img_metas,
          rescale=rescale,
      )
      return predictions
  
imports: from __future__ import division
  import copy
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from mmcv.ops import DeformConv2d
  from mmengine.config import ConfigDict
  from mmengine.model import BaseModule, ModuleList
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.structures import SampleList
  from mmdet.utils import ConfigType, InstanceList, MultiConfig, OptInstanceList, OptMultiConfig
  from ..task_modules.assigners import RegionAssigner
  from ..task_modules.samplers import PseudoSampler
  from ..utils import images_to_levels, multi_apply, select_single_mlvl, unpack_gt_instances
  from .base_dense_head import BaseDenseHead
  from .rpn_head import RPNHead
label_desc: Use image node_id 16 for calling the StageCascadeRPNHead obj's underlying member method code with example usage: obj.predict(x, batch_data_samples, rescale) and returns: predictions