class_docstr: ResNet backbone for DetectoRS.
  
  Args:
      sac (dict, optional): Dictionary to construct SAC (Switchable Atrous
          Convolution). Default: None.
      stage_with_sac (list): Which stage to use sac. Default: (False, False,
          False, False).
      rfp_inplanes (int, optional): The number of channels from RFP.
          Default: None. If specified, an additional conv layer will be
          added for ``rfp_feat``. Otherwise, the structure is the same as
          base class.
      output_img (bool): If ``True``, the input image will be inserted into
          the starting position of output. Default: False.
docstr: Pack all blocks in a stage into a ``ResLayer`` for DetectoRS.
docstr_tok: ["Pack","all","blocks","in","a","stage","into","a","``","ResLayer","``","for","DetectoRS","."]
code: def make_res_layer(self, **kwargs):
      """Pack all blocks in a stage into a ``ResLayer`` for DetectoRS."""
      return ResLayer(**kwargs)
  
imports: import torch.nn
  import torch.utils.checkpoint
  from mmcv.cnn import build_conv_layer, build_norm_layer
  from mmengine.logging import MMLogger
  from mmengine.model import Sequential, constant_init, kaiming_init
  from mmengine.runner.checkpoint import load_checkpoint
  from torch.nn.modules.batchnorm import _BatchNorm
  from mmdet.registry import MODELS
  from .resnet import BasicBlock
  from .resnet import Bottleneck
  from .resnet import ResNet
label_desc: Use image node_id 3 for calling the DetectoRS_ResNet obj's underlying member method code with example usage: obj.make_res_layer() and returns: ResLayer