text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  class DoubleConv(nn.Module):
      ''' ( conv => [BN] => ReLU ) * 2 '''
  
      def __init__(self, in_channels, out_channels, mid_channels=None):
          super(DoubleConv, self).__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(Down, self).__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, bilinear=True):
          super(Up, self).__init__()
  
          # if bilinear, use the normal convolutions to reduce the number of channels
          if bilinear:
              self.up = nn.Upsample(scale_factor=2, 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):
          x1 = self.up(x1)
  
          diff_h = x2.size(2) - x1.size(2)
          diff_w = x2.size(3) - x1.size(3)
  
          x1 = F.pad(x1, [diff_w // 2, diff_w - diff_w // 2, diff_h // 2, diff_h - diff_h // 2])
          x = torch.cat([x2, x1], dim=1)
  
  
  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)
  