code: def __init__(
      self,
      num_classes,
      min_channel=16,
      channel_ratio=1.0,
      add_edge=False,
  ):
      super().__init__()
      self.min_channel = min_channel
      self.channel_ratio = channel_ratio
      self.add_edge = add_edge
      self.deconv1 = nn.Conv2DTranspose(
          self.depth(96),
          self.depth(96),
          groups=1,
          kernel_size=4,
          stride=2,
          padding=1,
          bias_attr=False,
      )
      self.deconv2 = nn.Conv2DTranspose(
          self.depth(32),
          self.depth(32),
          groups=1,
          kernel_size=4,
          stride=2,
          padding=1,
          bias_attr=False,
      )
      self.deconv3 = nn.Conv2DTranspose(
          self.depth(24),
          self.depth(24),
          groups=1,
          kernel_size=4,
          stride=2,
          padding=1,
          bias_attr=False,
      )
      self.deconv4 = nn.Conv2DTranspose(
          self.depth(16),
          self.depth(16),
          groups=1,
          kernel_size=4,
          stride=2,
          padding=1,
          bias_attr=False,
      )
      self.deconv5 = nn.Conv2DTranspose(
          self.depth(8),
          self.depth(8),
          groups=1,
          kernel_size=4,
          stride=2,
          padding=1,
          bias_attr=False,
      )
  
      self.transit1 = ResidualBlock(self.depth(320), self.depth(96))
      self.transit2 = ResidualBlock(self.depth(96), self.depth(32))
      self.transit3 = ResidualBlock(self.depth(32), self.depth(24))
      self.transit4 = ResidualBlock(self.depth(24), self.depth(16))
      self.transit5 = ResidualBlock(self.depth(16), self.depth(8))
  
      self.pred = nn.Conv2D(
          self.depth(8), num_classes, 3, 1, 1, bias_attr=False
      )
      if self.add_edge:
          self.edge = nn.Conv2D(
              self.depth(8), num_classes, 3, 1, 1, bias_attr=False
          )
  
imports: import paddle.nn
  from paddleseg import utils
  from paddleseg.cvlibs import manager
label_desc: Use image node_id 1 to create a new PortraitNetHead object from inherited base classes: nn with example: obj = PortraitNetHead(num_classes, min_channel, channel_ratio, add_edge)