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: Forward function for RFP.
docstr_tok: ["Forward","function","for","RFP","."]
code: def rfp_forward(self, x, rfp_feats):
      """Forward function for RFP."""
      if self.deep_stem:
          x = self.stem(x)
      else:
          x = self.conv1(x)
          x = self.norm1(x)
          x = self.relu(x)
      x = self.maxpool(x)
      outs = []
      for i, layer_name in enumerate(self.res_layers):
          res_layer = getattr(self, layer_name)
          rfp_feat = rfp_feats[i] if i > 0 else None
          for layer in res_layer:
              x = layer.rfp_forward(x, rfp_feat)
          if i in self.out_indices:
              outs.append(x)
      return tuple(outs)
  
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 5 for calling the DetectoRS_ResNet obj's underlying member method code with example usage: obj.rfp_forward(x, rfp_feats) and returns: tuple