code: def __init__(
      self, in_channels, inter_channels, out_channels, lr_mult
  ):
      super().__init__()
      self.scale1 = nn.Sequential(
          nn.AvgPool2D(
              kernel_size=5, stride=2, padding=2, exclusive=False
          ),
          bn2d(in_channels, lr_mult=lr_mult),
          nn.ReLU(),
          conv2d(
              in_channels,
              inter_channels,
              kernel_size=1,
              lr_mult=lr_mult,
          ),
      )
      self.scale2 = nn.Sequential(
          nn.AvgPool2D(
              kernel_size=9, stride=4, padding=4, exclusive=False
          ),
          bn2d(in_channels, lr_mult=lr_mult),
          nn.ReLU(),
          conv2d(
              in_channels,
              inter_channels,
              kernel_size=1,
              lr_mult=lr_mult,
          ),
      )
      self.scale3 = nn.Sequential(
          nn.AvgPool2D(
              kernel_size=17, stride=8, padding=8, exclusive=False
          ),
          bn2d(in_channels, lr_mult=lr_mult),
          nn.ReLU(),
          conv2d(
              in_channels,
              inter_channels,
              kernel_size=1,
              lr_mult=lr_mult,
          ),
      )
      self.scale4 = nn.Sequential(
          nn.AdaptiveAvgPool2D((1, 1)),
          bn2d(in_channels, lr_mult=lr_mult),
          nn.ReLU(),
          conv2d(
              in_channels,
              inter_channels,
              kernel_size=1,
              lr_mult=lr_mult,
          ),
      )
      self.scale0 = nn.Sequential(
          bn2d(in_channels, lr_mult=lr_mult),
          nn.ReLU(),
          conv2d(
              in_channels,
              inter_channels,
              kernel_size=1,
              lr_mult=lr_mult,
          ),
      )
      self.process1 = nn.Sequential(
          bn2d(inter_channels, lr_mult=lr_mult),
          nn.ReLU(),
          conv2d(
              inter_channels,
              inter_channels,
              kernel_size=3,
              padding=1,
              lr_mult=lr_mult,
          ),
      )
      self.process2 = nn.Sequential(
          bn2d(inter_channels, lr_mult=lr_mult),
          nn.ReLU(),
          conv2d(
              inter_channels,
              inter_channels,
              kernel_size=3,
              padding=1,
              lr_mult=lr_mult,
          ),
      )
      self.process3 = nn.Sequential(
          bn2d(inter_channels, lr_mult=lr_mult),
          nn.ReLU(),
          conv2d(
              inter_channels,
              inter_channels,
              kernel_size=3,
              padding=1,
              lr_mult=lr_mult,
          ),
      )
      self.process4 = nn.Sequential(
          bn2d(inter_channels, lr_mult=lr_mult),
          nn.ReLU(),
          conv2d(
              inter_channels,
              inter_channels,
              kernel_size=3,
              padding=1,
              lr_mult=lr_mult,
          ),
      )
      self.compression = nn.Sequential(
          bn2d(inter_channels * 5, lr_mult=lr_mult),
          nn.ReLU(),
          conv2d(
              inter_channels * 5,
              out_channels,
              kernel_size=1,
              lr_mult=lr_mult,
          ),
      )
      self.shortcut = nn.Sequential(
          bn2d(in_channels, lr_mult=lr_mult),
          nn.ReLU(),
          conv2d(
              in_channels, out_channels, kernel_size=1, lr_mult=lr_mult
          ),
      )
  
imports: from functools import partial
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
  from paddleseg.utils import utils
  from paddleseg.models.backbones.transformer_utils import DropPath, Identity
  from paddleseg.cvlibs.param_init import constant_init, kaiming_normal_init, trunc_normal_init
label_desc: Use image node_id 1 to create a new DAPPM object from inherited base classes: nn with example: obj = DAPPM(in_channels, inter_channels, out_channels, lr_mult)