code: def __init__(self, in_chan: int, out_chan: int):
      super(AttentionRefinementModule, self).__init__()
      self.conv = layers.ConvBNReLU(
          in_chan, out_chan, kernel_size=3, stride=1, padding=1
      )
      self.conv_atten = nn.Conv2D(
          out_chan, out_chan, kernel_size=1, bias_attr=None
      )
      self.bn_atten = nn.BatchNorm2D(out_chan)
      self.sigmoid_atten = nn.Sigmoid()
  
imports: import os
  from typing import Union, List, Tuple
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddlehub.module.module import moduleinfo
  import paddlehub.vision.segmentation_transforms
  from paddlehub.module.cv_module import ImageSegmentationModule
  from stdc1_seg_voc.stdcnet import STDC1
  import stdc1_seg_voc.layers
label_desc: Use image node_id 1 to create a new AttentionRefinementModule object from inherited base classes: nn with example: obj = AttentionRefinementModule(in_chan, out_chan)