code: def forward(self, features):
      features[1:] = [
          conv1x1(feature)
          for feature, conv1x1 in zip(features[1:], self.conv1x1)
      ]
      P = [
          up_and_add(features[i], features[i - 1])
          for i in reversed(range(1, len(features)))
      ]
      P = [
          smooth_conv(x) for smooth_conv, x in zip(self.smooth_conv, P)
      ]
      P = list(reversed(P))
      P.append(features[-1])  # P = [P1, P2, P3, P4]
      H, W = P[0].size(2), P[0].size(3)
      P[1:] = [
          F.interpolate(
              feature, size=(H, W), mode="bilinear", align_corners=True
          )
          for feature in P[1:]
      ]
  
      x = self.conv_fusion(torch.cat((P), dim=1))
      return x
  
imports: import torch
  import logging
  import torch.nn
  import torch.nn.functional
  from torchvision import models
  from itertools import chain
  import numpy
  from collections import OrderedDict
label_desc: Use image node_id 2 for calling the FPN_fuse obj's underlying member method code with example usage: obj.forward(features) and returns: x