code: def __init__(self, in_channels, **kwargs):
      super(SAST_Header1, self).__init__()
      out_channels = [64, 64, 128]
      self.score_conv = nn.Sequential(
          ConvBNLayer(
              in_channels,
              out_channels[0],
              1,
              1,
              act="relu",
              name="f_score1",
          ),
          ConvBNLayer(
              out_channels[0],
              out_channels[1],
              3,
              1,
              act="relu",
              name="f_score2",
          ),
          ConvBNLayer(
              out_channels[1],
              out_channels[2],
              1,
              1,
              act="relu",
              name="f_score3",
          ),
          ConvBNLayer(
              out_channels[2], 1, 3, 1, act=None, name="f_score4"
          ),
      )
      self.border_conv = nn.Sequential(
          ConvBNLayer(
              in_channels,
              out_channels[0],
              1,
              1,
              act="relu",
              name="f_border1",
          ),
          ConvBNLayer(
              out_channels[0],
              out_channels[1],
              3,
              1,
              act="relu",
              name="f_border2",
          ),
          ConvBNLayer(
              out_channels[1],
              out_channels[2],
              1,
              1,
              act="relu",
              name="f_border3",
          ),
          ConvBNLayer(
              out_channels[2], 4, 3, 1, act=None, name="f_border4"
          ),
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import paddle
  from paddle import nn
  import paddle.nn.functional
  from paddle import ParamAttr
label_desc: Use image node_id 1 to create a new SAST_Header1 object from inherited base classes: nn with example: obj = SAST_Header1(in_channels)