class_docstr: The U^2-Net implementation based on PaddlePaddle.
  
  The original article refers to
  Xuebin Qin, et, al. "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection"
  (https://arxiv.org/abs/2005.09007).
  
  Args:
      num_classes (int): The unique number of target classes.
      in_channels (int, optional): Input channels. Default: 3.
      pretrained (str, optional): The path or url of pretrained model for fine tuning. Default: None.
code: def forward(self, x):
      hx = x
  
      # stage 1
      hx1 = self.stage1(hx)
      hx = self.pool12(hx1)
  
      # stage 2
      hx2 = self.stage2(hx)
      hx = self.pool23(hx2)
  
      # stage 3
      hx3 = self.stage3(hx)
      hx = self.pool34(hx3)
  
      # stage 4
      hx4 = self.stage4(hx)
      hx = self.pool45(hx4)
  
      # stage 5
      hx5 = self.stage5(hx)
      hx = self.pool56(hx5)
  
      # stage 6
      hx6 = self.stage6(hx)
      hx6up = _upsample_like(hx6, hx5)
  
      # -------------------- decoder --------------------
      hx5d = self.stage5d(paddle.concat((hx6up, hx5), 1))
      hx5dup = _upsample_like(hx5d, hx4)
  
      hx4d = self.stage4d(paddle.concat((hx5dup, hx4), 1))
      hx4dup = _upsample_like(hx4d, hx3)
  
      hx3d = self.stage3d(paddle.concat((hx4dup, hx3), 1))
      hx3dup = _upsample_like(hx3d, hx2)
  
      hx2d = self.stage2d(paddle.concat((hx3dup, hx2), 1))
      hx2dup = _upsample_like(hx2d, hx1)
  
      hx1d = self.stage1d(paddle.concat((hx2dup, hx1), 1))
  
      # side output
      d1 = self.side1(hx1d)
  
      d2 = self.side2(hx2d)
      d2 = _upsample_like(d2, d1)
  
      d3 = self.side3(hx3d)
      d3 = _upsample_like(d3, d1)
  
      d4 = self.side4(hx4d)
      d4 = _upsample_like(d4, d1)
  
      d5 = self.side5(hx5d)
      d5 = _upsample_like(d5, d1)
  
      d6 = self.side6(hx6)
      d6 = _upsample_like(d6, d1)
  
      d0 = self.outconv(paddle.concat((d1, d2, d3, d4, d5, d6), 1))
  
      return [d0, d1, d2, d3, d4, d5, d6]
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 2 for calling the U2Net obj's underlying member method code with example usage: obj.forward(x) and returns: list