text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  class UNet(nn.Module):
      def __init__(self, n_channels, n_classes):
          super(UNet, self).__init__()
          self.inc = inconv(n_channels, 64)
          self.down1 = down(64, 128)
          self.down2 = down(128, 256)
          self.down3 = down(256, 512)
          self.down4 = down(512, 512)
          self.up1 = up(1024, 256)
          self.up2 = up(512, 128)
          self.up3 = up(256, 64)
          self.up4 = up(128, 64)
          self.outc = outconv(64, n_classes)
  
      def forward(self, x):
          x1 = self.inc(x)
          x2 = self.down1(x1)
          x3 = self.down2(x2)
          x4 = self.down3(x3)
          x5 = self.down4(x4)
          x = self.up1(x5, x4)
          x = self.up2(x, x3)
          x = self.up3(x, x2)
          x = self.up4(x, x1)
          x = self.outc(x)
          return torch.sigmoid(x)
  
  
  class double_conv(nn.Module):
      '''(conv => BN => ReLU) * 2'''
      def __init__(self, in_ch, out_ch):
          super(double_conv, self).__init__()
          self.conv = nn.Sequential(
              nn.Conv2d(in_ch, out_ch, 3, padding=1),
              nn.BatchNorm2d(out_ch),
              nn.ReLU(inplace=True),
              nn.Conv2d(out_ch, out_ch, 3, padding=1),
              nn.BatchNorm2d(out_ch),
              nn.ReLU(inplace=True)
          )
  
      def forward(self, x):
          x = self.conv(x)
          return x
  
  
  class inconv(nn.Module):
      def __init__(self, in_ch, out_ch):
          super(inconv, self).__init__()
          self.conv = double_conv(in_ch, out_ch)
  
      def forward(self, x):
          x = self.conv(x)
          return x
  
  
  class down(nn.Module):
      def __init__(self, in_ch, out_ch):
          super(down, self).__init__()
          self.mpconv = nn.Sequential(
              nn.MaxPool2d(2),
              double_conv(in_ch, out_ch)
          )
  
      def forward(self, x):
          x = self.mpconv(x)
          return x
  
  
  class up(nn.Module):
      def __init__(self, in_ch, out_ch, bilinear=False):
          super(up, self).__init__()
          if bilinear:
              self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
          else:
              self.up = nn.ConvTranspose2d(in_ch // 2, in_ch // 2, 2, stride=2)
  
          self.conv = double_conv(in_ch, out_ch)
  
      def forward(self, x1, x2):
          x1 = self.up(x1)
  
          # input is CHW
          diffY = x2.size()[2] - x1.size()[2]
          diffX = x2.size()[3] - x1.size()[3]
  
          x1 = F.pad(x1, (diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2))
  
          x = torch.cat([x2, x1], dim=1)
          x = self.conv(x)
          return x
  
  
  class outconv(nn.Module):
      def __init__(self, in_ch, out_ch):
          super(outconv, self).__init__()
          self.conv = nn.Conv2d(in_ch, out_ch, 1)
  
      def forward(self, x):
          x = self.conv(x)
          return x
  
  #net = UNet(3,1)
  #print(net)