class_docstr: ResNet+FPN, output resolution are 1/16 and 1/4.
  
  Each block has 2 layers.
class_docstr_tok: ["ResNet+FPN",",","output","resolution","are","1\/16","and","1\/4",".","Each","block","has","2","layers","."]
code: def forward(self, x: Tensor) -> List[Tensor]:
      # ResNet Backbone
      x0 = self.relu(self.bn1(self.conv1(x)))
      x1 = self.layer1(x0)  # 1/2
      x2 = self.layer2(x1)  # 1/4
      x3 = self.layer3(x2)  # 1/8
      x4 = self.layer4(x3)  # 1/16
  
      # FPN
      x4_out = self.layer4_outconv(x4)
  
      x4_out_2x = F.interpolate(
          x4_out, scale_factor=2.0, mode="bilinear", align_corners=True
      )
      x3_out = self.layer3_outconv(x3)
      x3_out = self.layer3_outconv2(x3_out + x4_out_2x)
  
      x3_out_2x = F.interpolate(
          x3_out, scale_factor=2.0, mode="bilinear", align_corners=True
      )
      x2_out = self.layer2_outconv(x2)
      x2_out = self.layer2_outconv2(x2_out + x3_out_2x)
  
      return [x4_out, x2_out]
  
file_path: kornia/kornia/feature/loftr/backbone/resnet_fpn.py
imports: from typing import Any, Dict, List, Type, Union
  import torch.nn.functional
  from torch import nn
  from kornia.core import Module, Tensor
label_desc: Use image node_id 3 for calling the ResNetFPN_16_4 obj's underlying member method code with example usage: obj.forward(x) and returns: list