text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from ..utils import downsample_block
  
  
  class P2PDiscriminator(nn.Module):
      def __init__(self, img_size, in_channels=3):
          super(P2PDiscriminator, self).__init__()
  
          self.model = nn.Sequential(
              *downsample_block(in_channels * 2, 64, bn=False),
              *downsample_block(64, 128),
              *downsample_block(128, 256),
              *downsample_block(256, 512),
              nn.ZeroPad2d((1, 0, 1, 0)),
              nn.Conv2d(512, 1, 4, padding=1, bias=False)
          )
  
      def forward(self, img_A, img_B):
          img_input = torch.cat((img_A, img_B), 1)
          return self.model(img_input)
  
  