code: def __init__(
      self, in_channels, out_channels, use_asf=False, **kwargs
  ):
      super(DBFPN, self).__init__()
      self.out_channels = out_channels
      self.use_asf = use_asf
      weight_attr = paddle.nn.initializer.KaimingUniform()
  
      self.in2_conv = nn.Conv2D(
          in_channels=in_channels[0],
          out_channels=self.out_channels,
          kernel_size=1,
          weight_attr=ParamAttr(initializer=weight_attr),
          bias_attr=False,
      )
      self.in3_conv = nn.Conv2D(
          in_channels=in_channels[1],
          out_channels=self.out_channels,
          kernel_size=1,
          weight_attr=ParamAttr(initializer=weight_attr),
          bias_attr=False,
      )
      self.in4_conv = nn.Conv2D(
          in_channels=in_channels[2],
          out_channels=self.out_channels,
          kernel_size=1,
          weight_attr=ParamAttr(initializer=weight_attr),
          bias_attr=False,
      )
      self.in5_conv = nn.Conv2D(
          in_channels=in_channels[3],
          out_channels=self.out_channels,
          kernel_size=1,
          weight_attr=ParamAttr(initializer=weight_attr),
          bias_attr=False,
      )
      self.p5_conv = nn.Conv2D(
          in_channels=self.out_channels,
          out_channels=self.out_channels // 4,
          kernel_size=3,
          padding=1,
          weight_attr=ParamAttr(initializer=weight_attr),
          bias_attr=False,
      )
      self.p4_conv = nn.Conv2D(
          in_channels=self.out_channels,
          out_channels=self.out_channels // 4,
          kernel_size=3,
          padding=1,
          weight_attr=ParamAttr(initializer=weight_attr),
          bias_attr=False,
      )
      self.p3_conv = nn.Conv2D(
          in_channels=self.out_channels,
          out_channels=self.out_channels // 4,
          kernel_size=3,
          padding=1,
          weight_attr=ParamAttr(initializer=weight_attr),
          bias_attr=False,
      )
      self.p2_conv = nn.Conv2D(
          in_channels=self.out_channels,
          out_channels=self.out_channels // 4,
          kernel_size=3,
          padding=1,
          weight_attr=ParamAttr(initializer=weight_attr),
          bias_attr=False,
      )
  
      if self.use_asf is True:
          self.asf = ASFBlock(self.out_channels, self.out_channels // 4)
  
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
  from ppocr.modeling.necks.intracl import IntraCLBlock
  from ppocr.modeling.backbones.det_mobilenet_v3 import SEModule
label_desc: Use image node_id 1 to create a new DBFPN object from inherited base classes: nn with example: obj = DBFPN(in_channels, out_channels, use_asf)