code: def forward(self, x, mask_features=None):
      x = self.conv1(x)
      x = self.bn1(x)
      x = self.relu(x)
      if mask_features is not None:
          x = x + mask_features
      x = self.maxpool(x)
      c1 = self.layer1(x)
      c2 = self.layer2(c1)
      c3 = self.layer3(c2)
      c4 = self.layer4(c3)
  
      return c1, c2, c3, c4
  
file_path: carefree-learn/cflearn/api/cv/third_party/iharm/model/modeling/resnet.py
imports: import torch
  from .resnetv1b import resnet34_v1b
  from .resnetv1b import resnet50_v1s
  from .resnetv1b import resnet101_v1s
  from .resnetv1b import resnet152_v1s
label_desc: Use image node_id 2 for calling the ResNetBackbone obj's underlying member method code with example usage: obj.forward(x, mask_features) and returns: c1,  c2,  c3,  c4