code: def __init__(self, in_channels, out_channel=128):
      super(CTFPN, self).__init__()
      self.out_channels = out_channel * 4
  
      self.reduce_layer1 = Conv_BN_ReLU(in_channels[0], 128)
      self.reduce_layer2 = Conv_BN_ReLU(in_channels[1], 128)
      self.reduce_layer3 = Conv_BN_ReLU(in_channels[2], 128)
      self.reduce_layer4 = Conv_BN_ReLU(in_channels[3], 128)
  
      self.fpem1 = FPEM(
          in_channels=(64, 128, 256, 512), out_channels=128
      )
      self.fpem2 = FPEM(
          in_channels=(64, 128, 256, 512), out_channels=128
      )
  
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 1 to create a new CTFPN object from inherited base classes: nn with example: obj = CTFPN(in_channels, out_channel)