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.
code: def init_weights(self):
      # Calling this method will cause parameter initialization exception
      # super(DetectoRS_ResNet, self).init_weights()
  
      if isinstance(self.pretrained, str):
          logger = MMLogger.get_current_instance()
          load_checkpoint(
              self, self.pretrained, strict=False, logger=logger
          )
      elif self.pretrained is None:
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  kaiming_init(m)
              elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
                  constant_init(m, 1)
  
          if self.dcn is not None:
              for m in self.modules():
                  if isinstance(m, Bottleneck) and hasattr(
                      m.conv2, "conv_offset"
                  ):
                      constant_init(m.conv2.conv_offset, 0)
  
          if self.zero_init_residual:
              for m in self.modules():
                  if isinstance(m, Bottleneck):
                      constant_init(m.norm3, 0)
                  elif isinstance(m, BasicBlock):
                      constant_init(m.norm2, 0)
      else:
          raise TypeError("pretrained must be a str or None")
  
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 2 for calling the DetectoRS_ResNet obj's underlying member method code with example usage: obj.init_weights() without return types