text: import torch
  import torch.nn as nn
  
  
  class DualCov(nn.Module):
      def __init__(self, in_channels, middle_channels, out_channels):
          super().__init__()
          self.relu = nn.ReLU(inplace=True)
          self.conv1 = nn.Conv2d(in_channels, middle_channels, 3, padding=1)
          self.bn1 = nn.BatchNorm2d(middle_channels)
          self.conv2 = nn.Conv2d(middle_channels, out_channels, 3, padding=1)
          self.bn2 = nn.BatchNorm2d(out_channels)
  
      def forward(self, x):
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
          out = self.conv2(out)
          out = self.bn2(out)
          out = self.relu(out)
          return out
  
  
  class UNet(nn.Module):
      def __init__(self, num_classes, input_channels=3, **kwargs):
          super().__init__()
  
          nb_filter = [32, 64, 128, 256, 512]
  
          self.pool = nn.MaxPool2d(2, 2)
          self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
  
          self.conv0_0 = DualCov(input_channels, nb_filter[0], nb_filter[0])
          self.conv1_0 = DualCov(nb_filter[0], nb_filter[1], nb_filter[1])
          self.conv2_0 = DualCov(nb_filter[1], nb_filter[2], nb_filter[2])
          self.conv3_0 = DualCov(nb_filter[2], nb_filter[3], nb_filter[3])
          self.conv4_0 = DualCov(nb_filter[3], nb_filter[4], nb_filter[4])
  
          self.conv3_1 = DualCov(nb_filter[3]+nb_filter[4], nb_filter[3], nb_filter[3])
          self.conv2_2 = DualCov(nb_filter[2]+nb_filter[3], nb_filter[2], nb_filter[2])
          self.conv1_3 = DualCov(nb_filter[1]+nb_filter[2], nb_filter[1], nb_filter[1])
          self.conv0_4 = DualCov(nb_filter[0]+nb_filter[1], nb_filter[0], nb_filter[0])
  
          self.final = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)
  
  
      def forward(self, x):
          x0_0 = self.conv0_0(x)
          x1_0 = self.conv1_0(self.pool(x0_0))
          x2_0 = self.conv2_0(self.pool(x1_0))
          x3_0 = self.conv3_0(self.pool(x2_0))
          x4_0 = self.conv4_0(self.pool(x3_0))
  
          x3_1 = self.conv3_1(torch.cat([x3_0, self.up(x4_0)], 1))
          x2_2 = self.conv2_2(torch.cat([x2_0, self.up(x3_1)], 1))
          x1_3 = self.conv1_3(torch.cat([x1_0, self.up(x2_2)], 1))
          x0_4 = self.conv0_4(torch.cat([x0_0, self.up(x1_3)], 1))
  
          output = self.final(x0_4)
          return output
  
  
  
  
  