class_docstr: The neck used in `CenterNet <https://arxiv.org/abs/1904.07850>`_ for
  object classification and box regression.
  
  Args:
       in_channels (int): Number of input channels.
       num_deconv_filters (tuple[int]): Number of filters per stage.
       num_deconv_kernels (tuple[int]): Number of kernels per stage.
       use_dcn (bool): If True, use DCNv2. Defaults to True.
       init_cfg (:obj:`ConfigDict` or dict or list[dict] or
           list[:obj:`ConfigDict`], optional): Initialization
           config dict.
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code: def __init__(
      self,
      in_channels: int,
      num_deconv_filters: Tuple[int, ...],
      num_deconv_kernels: Tuple[int, ...],
      use_dcn: bool = True,
      init_cfg: OptMultiConfig = None,
  ) -> None:
      super().__init__(init_cfg=init_cfg)
      assert len(num_deconv_filters) == len(num_deconv_kernels)
      self.fp16_enabled = False
      self.use_dcn = use_dcn
      self.in_channels = in_channels
      self.deconv_layers = self._make_deconv_layer(
          num_deconv_filters, num_deconv_kernels
      )
  
file_path: mmdetection/mmdet/models/necks/ct_resnet_neck.py
imports: import math
  from typing import Sequence, Tuple
  import torch
  import torch.nn
  from mmcv.cnn import ConvModule
  from mmengine.model import BaseModule
  from mmdet.registry import MODELS
  from mmdet.utils import OptMultiConfig
label_desc: Use image node_id 1 to create a new CTResNetNeck object from inherited base classes: BaseModule with example: obj = CTResNetNeck(in_channels, num_deconv_filters, num_deconv_kernels, use_dcn, init_cfg)