code: def forward(self, net: Tensor) -> Tensor:
      hx = net
  
      hxin = self.conv_in(hx)
      # hx = self.pool_in(hxin)
  
      # stage 1
      hx1 = self.stage1(hxin)
      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(torch.cat((hx6up, hx5), 1))
      hx5dup = _upsample_like(hx5d, hx4)
  
      hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
      hx4dup = _upsample_like(hx4d, hx3)
  
      hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
      hx3dup = _upsample_like(hx3d, hx2)
  
      hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
      hx2dup = _upsample_like(hx2d, hx1)
  
      hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))
  
      # side output
      d1 = self.side1(hx1d)
      d1 = _upsample_like(d1, net)
  
      d2 = self.side2(hx2d)
      d2 = _upsample_like(d2, net)
  
      d3 = self.side3(hx3d)
      d3 = _upsample_like(d3, net)
  
      d4 = self.side4(hx4d)
      d4 = _upsample_like(d4, net)
  
      d5 = self.side5(hx5d)
      d5 = _upsample_like(d5, net)
  
      d6 = self.side6(hx6)
      d6 = _upsample_like(d6, net)
  
      # d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1))
  
      return [
          torch.sigmoid(d1),
          torch.sigmoid(d2),
          torch.sigmoid(d3),
          torch.sigmoid(d4),
          torch.sigmoid(d5),
          torch.sigmoid(d6),
      ], [hx1d, hx2d, hx3d, hx4d, hx5d, hx6]
  
imports: import torch
  import numpy
  import torch.nn
  import torch.nn.functional
  from PIL import Image
  from torch import Tensor
  from cftool.cv import to_rgb
  from torchvision.transforms.functional import normalize
  from ....misc.toolkit import download_model
label_desc: Use image node_id 2 for calling the ISNetDIS obj's underlying member method code with example usage: obj.forward(net) and returns: list,  list