code: def forward(self, f):
      # # reduce channel
      f1 = self.reduce_layer1(
          f[0]
      )  # N,64,160,160    --> N, 128, 160, 160
      f2 = self.reduce_layer2(
          f[1]
      )  # N, 128, 80, 80  --> N, 128, 80, 80
      f3 = self.reduce_layer3(
          f[2]
      )  # N, 256, 40, 40  --> N, 128, 40, 40
      f4 = self.reduce_layer4(
          f[3]
      )  # N, 512, 20, 20  --> N, 128, 20, 20
  
      # FPEM
      f1_1, f2_1, f3_1, f4_1 = self.fpem1(f1, f2, f3, f4)
      f1_2, f2_2, f3_2, f4_2 = self.fpem2(f1_1, f2_1, f3_1, f4_1)
  
      # FFM
      f1 = f1_1 + f1_2
      f2 = f2_1 + f2_2
      f3 = f3_1 + f3_2
      f4 = f4_1 + f4_2
  
      f2 = self._upsample(f2, scale=2)
      f3 = self._upsample(f3, scale=4)
      f4 = self._upsample(f4, scale=8)
      ff = paddle.concat((f1, f2, f3, f4), 1)  # N,512, 160,160
      return ff
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from paddle import nn
  import paddle.nn.functional
  from paddle import ParamAttr
  import os
  import sys
  import math
  from paddle.nn.initializer import TruncatedNormal, Constant, Normal
label_desc: Use image node_id 3 for calling the CTFPN obj's underlying member method code with example usage: obj.forward(f) and returns: ff