text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  class DoubleConv(nn.Module):
      """(convolution => [BN] => ReLU) * 2"""
  
      def __init__(self, in_channels, out_channels, mid_channels=None):
          super().__init__()
          if not mid_channels:
              mid_channels = out_channels
          self.double_conv = nn.Sequential(
              nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
              nn.BatchNorm2d(mid_channels),
              nn.ReLU(inplace=True),
              nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),
              nn.BatchNorm2d(out_channels),
              nn.ReLU(inplace=True)
          )
  
      def forward(self, x):
          return self.double_conv(x)
  
  
  class Down(nn.Module):
      """Downscaling with maxpool then double conv"""
  
      def __init__(self, in_channels, out_channels):
          super().__init__()
          self.maxpool_conv = nn.Sequential(
              nn.MaxPool2d(2),
              DoubleConv(in_channels, out_channels)
          )
  
      def forward(self, x):
          return self.maxpool_conv(x)
  
  
  class Up(nn.Module):
      """Upscaling then double conv"""
  
      def __init__(self, in_channels, out_channels, scale_factor=2, bilinear=True):
          super().__init__()
  
          # if bilinear, use the normal convolutions to reduce the number of channels
          if bilinear:
              self.up = nn.Upsample(scale_factor=scale_factor, mode='bilinear', align_corners=True)
              self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
          else:
              self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2)
              self.conv = DoubleConv(in_channels, out_channels)
  
  
      def forward(self, x1, x2=None):
          x = 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])
          # if you have padding issues, see
          # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
          # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
          '''
          if x2 is not None:
              x = torch.cat([x2, x], dim=1)
          return self.conv(x)
  
  
  class InConv(nn.Module):
      def __init__(self, in_channels, out_channels):
          super(InConv, self).__init__()
          self.conv = DoubleConv(in_channels, out_channels)
  
      def forward(self, x):
          return self.conv(x)
  
  
  
  class OutConv(nn.Module):
      def __init__(self, in_channels, out_channels):
          super(OutConv, self).__init__()
          self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
  
      def forward(self, x):
          return self.conv(x)
  