code: def __init__(
      self,
      backbone,
      neck=None,
      query_head=None,  # detr head
      rpn_head=None,  # two-stage rpn
      roi_head=[None],  # two-stage
      bbox_head=[None],  # one-stage
      train_cfg=[None, None],
      test_cfg=[None, None],
      # Control whether to consider positive samples
      # from the auxiliary head as additional positive queries.
      with_pos_coord=True,
      use_lsj=True,
      eval_module="detr",
      # Evaluate the Nth head.
      eval_index=0,
      data_preprocessor: OptConfigType = None,
      init_cfg: OptMultiConfig = None,
  ):
      super(CoDETR, self).__init__(
          data_preprocessor=data_preprocessor, init_cfg=init_cfg
      )
      self.with_pos_coord = with_pos_coord
      self.use_lsj = use_lsj
  
      assert eval_module in ["detr", "one-stage", "two-stage"]
      self.eval_module = eval_module
  
      self.backbone = MODELS.build(backbone)
      if neck is not None:
          self.neck = MODELS.build(neck)
      # Module index for evaluation
      self.eval_index = eval_index
      head_idx = 0
      if query_head is not None:
          query_head.update(
              train_cfg=train_cfg[head_idx]
              if (
                  train_cfg is not None
                  and train_cfg[head_idx] is not None
              )
              else None
          )
          query_head.update(test_cfg=test_cfg[head_idx])
          self.query_head = MODELS.build(query_head)
          self.query_head.init_weights()
          head_idx += 1
  
      if rpn_head is not None:
          rpn_train_cfg = (
              train_cfg[head_idx].rpn
              if (
                  train_cfg is not None
                  and train_cfg[head_idx] is not None
              )
              else None
          )
          rpn_head_ = rpn_head.copy()
          rpn_head_.update(
              train_cfg=rpn_train_cfg, test_cfg=test_cfg[head_idx].rpn
          )
          self.rpn_head = MODELS.build(rpn_head_)
          self.rpn_head.init_weights()
  
      self.roi_head = nn.ModuleList()
      for i in range(len(roi_head)):
          if roi_head[i]:
              rcnn_train_cfg = (
                  train_cfg[i + head_idx].rcnn
                  if (train_cfg and train_cfg[i + head_idx] is not None)
                  else None
              )
              roi_head[i].update(train_cfg=rcnn_train_cfg)
              roi_head[i].update(test_cfg=test_cfg[i + head_idx].rcnn)
              self.roi_head.append(MODELS.build(roi_head[i]))
              self.roi_head[-1].init_weights()
  
      self.bbox_head = nn.ModuleList()
      for i in range(len(bbox_head)):
          if bbox_head[i]:
              bbox_head[i].update(
                  train_cfg=train_cfg[i + head_idx + len(self.roi_head)]
                  if (
                      train_cfg
                      and train_cfg[i + head_idx + len(self.roi_head)]
                      is not None
                  )
                  else None
              )
              bbox_head[i].update(
                  test_cfg=test_cfg[i + head_idx + len(self.roi_head)]
              )
              self.bbox_head.append(MODELS.build(bbox_head[i]))
              self.bbox_head[-1].init_weights()
  
      self.head_idx = head_idx
      self.train_cfg = train_cfg
      self.test_cfg = test_cfg
  
imports: import copy
  from typing import Tuple, Union
  import torch
  import torch.nn
  from torch import Tensor
  from mmdet.models.detectors.base import BaseDetector
  from mmdet.registry import MODELS
  from mmdet.structures import OptSampleList, SampleList
  from mmdet.utils import InstanceList, OptConfigType, OptMultiConfig
label_desc: Use image node_id 1 to create a new CoDETR object from inherited base classes: BaseDetector with example: obj = CoDETR(backbone, neck, query_head, rpn_head, roi_head, bbox_head, train_cfg, test_cfg, with_pos_coord, use_lsj, eval_module, eval_index, data_preprocessor, init_cfg)