code: def __init__(self, filters, cat_channels, up_channels):
      super(Decoder, self).__init__()
      """stage 4d"""
      # h1->320*320, hd4->40*40, Pooling 8 times
      self.h1_PT_hd4 = nn.MaxPool2D(8, 8, ceil_mode=True)
      self.h1_PT_hd4_cbr = ConvBnReLU2D(filters[0], cat_channels)
      # h2->160*160, hd4->40*40, Pooling 4 times
      self.h2_PT_hd4 = nn.MaxPool2D(4, 4, ceil_mode=True)
      self.h2_PT_hd4_cbr = ConvBnReLU2D(filters[1], cat_channels)
      # h3->80*80, hd4->40*40, Pooling 2 times
      self.h3_PT_hd4 = nn.MaxPool2D(2, 2, ceil_mode=True)
      self.h3_PT_hd4_cbr = ConvBnReLU2D(filters[2], cat_channels)
      # h4->40*40, hd4->40*40, Concatenation
      self.h4_Cat_hd4_cbr = ConvBnReLU2D(filters[3], cat_channels)
      # hd5->20*20, hd4->40*40, Upsample 2 times
      self.hd5_UT_hd4 = nn.Upsample(
          scale_factor=2, mode="bilinear"
      )  # 14*14
      self.hd5_UT_hd4_cbr = ConvBnReLU2D(filters[4], cat_channels)
      # fusion(h1_PT_hd4, h2_PT_hd4, h3_PT_hd4, h4_Cat_hd4, hd5_UT_hd4)
      self.cbr4d_1 = ConvBnReLU2D(up_channels, up_channels)  # 16
      """stage 3d"""
      # h1->320*320, hd3->80*80, Pooling 4 times
      self.h1_PT_hd3 = nn.MaxPool2D(4, 4, ceil_mode=True)
      self.h1_PT_hd3_cbr = ConvBnReLU2D(filters[0], cat_channels)
      # h2->160*160, hd3->80*80, Pooling 2 times
      self.h2_PT_hd3 = nn.MaxPool2D(2, 2, ceil_mode=True)
      self.h2_PT_hd3_cbr = ConvBnReLU2D(filters[1], cat_channels)
      # h3->80*80, hd3->80*80, Concatenation
      self.h3_Cat_hd3_cbr = ConvBnReLU2D(filters[2], cat_channels)
      # hd4->40*40, hd4->80*80, Upsample 2 times
      self.hd4_UT_hd3 = nn.Upsample(
          scale_factor=2, mode="bilinear"
      )  # 14*14
      self.hd4_UT_hd3_cbr = ConvBnReLU2D(up_channels, cat_channels)
      # hd5->20*20, hd4->80*80, Upsample 4 times
      self.hd5_UT_hd3 = nn.Upsample(
          scale_factor=4, mode="bilinear"
      )  # 14*14
      self.hd5_UT_hd3_cbr = ConvBnReLU2D(filters[4], cat_channels)
      # fusion(h1_PT_hd3, h2_PT_hd3, h3_Cat_hd3, hd4_UT_hd3, hd5_UT_hd3)
      self.cbr3d_1 = ConvBnReLU2D(up_channels, up_channels)  # 16
      """stage 2d """
      # h1->320*320, hd2->160*160, Pooling 2 times
      self.h1_PT_hd2 = nn.MaxPool2D(2, 2, ceil_mode=True)
      self.h1_PT_hd2_cbr = ConvBnReLU2D(filters[0], cat_channels)
      # h2->160*160, hd2->160*160, Concatenation
      self.h2_Cat_hd2_cbr = ConvBnReLU2D(filters[1], cat_channels)
      # hd3->80*80, hd2->160*160, Upsample 2 times
      self.hd3_UT_hd2 = nn.Upsample(
          scale_factor=2, mode="bilinear"
      )  # 14*14
      self.hd3_UT_hd2_cbr = ConvBnReLU2D(up_channels, cat_channels)
      # hd4->40*40, hd2->160*160, Upsample 4 times
      self.hd4_UT_hd2 = nn.Upsample(
          scale_factor=4, mode="bilinear"
      )  # 14*14
      self.hd4_UT_hd2_cbr = ConvBnReLU2D(up_channels, cat_channels)
      # hd5->20*20, hd2->160*160, Upsample 8 times
      self.hd5_UT_hd2 = nn.Upsample(
          scale_factor=8, mode="bilinear"
      )  # 14*14
      self.hd5_UT_hd2_cbr = ConvBnReLU2D(filters[4], cat_channels)
      # fusion(h1_PT_hd2, h2_Cat_hd2, hd3_UT_hd2, hd4_UT_hd2, hd5_UT_hd2)
      self.cbr2d_1 = ConvBnReLU2D(up_channels, up_channels)  # 16
      """stage 1d"""
      # h1->320*320, hd1->320*320, Concatenation
      self.h1_Cat_hd1_cbr = ConvBnReLU2D(filters[0], cat_channels)
      # hd2->160*160, hd1->320*320, Upsample 2 times
      self.hd2_UT_hd1 = nn.Upsample(
          scale_factor=2, mode="bilinear"
      )  # 14*14
      self.hd2_UT_hd1_cbr = ConvBnReLU2D(up_channels, cat_channels)
      # hd3->80*80, hd1->320*320, Upsample 4 times
      self.hd3_UT_hd1 = nn.Upsample(
          scale_factor=4, mode="bilinear"
      )  # 14*14
      self.hd3_UT_hd1_cbr = ConvBnReLU2D(up_channels, cat_channels)
      # hd4->40*40, hd1->320*320, Upsample 8 times
      self.hd4_UT_hd1 = nn.Upsample(
          scale_factor=8, mode="bilinear"
      )  # 14*14
      self.hd4_UT_hd1_cbr = ConvBnReLU2D(up_channels, cat_channels)
      # hd5->20*20, hd1->320*320, Upsample 16 times
      self.hd5_UT_hd1 = nn.Upsample(
          scale_factor=16, mode="bilinear"
      )  # 14*14
      self.hd5_UT_hd1_cbr = ConvBnReLU2D(filters[4], cat_channels)
      # fusion(h1_Cat_hd1, hd2_UT_hd1, hd3_UT_hd1, hd4_UT_hd1, hd5_UT_hd1)
      self.cbr1d_1 = ConvBnReLU2D(up_channels, up_channels)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models.layers.layer_libs import SyncBatchNorm
  from paddleseg.cvlibs.param_init import kaiming_normal_init
label_desc: Use image node_id 1 to create a new Decoder object from inherited base classes: nn with example: obj = Decoder(filters, cat_channels, up_channels)