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.
class_docstr_tok: ["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","."]
docstr: model forward.
docstr_tok: ["model","forward","."]
code: def forward(self, x: Sequence[torch.Tensor]) -> Tuple[torch.Tensor]:
      """model forward."""
      assert isinstance(x, (list, tuple))
      outs = self.deconv_layers(x[-1])
      return (outs,)
  
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 4 for calling the CTResNetNeck obj's underlying member method code with example usage: obj.forward(x) and returns: outs