text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from .warplayer import warp
  
  def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
      return nn.Sequential(
          torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1),
          nn.PReLU(out_planes)
      )
  
  def conv_wo_act(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
      return nn.Sequential(
          nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
                    padding=padding, dilation=dilation, bias=True),
          )
  
  def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
      return nn.Sequential(
          nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
                    padding=padding, dilation=dilation, bias=True),
          nn.PReLU(out_planes)
      )
  
  class IFBlock(nn.Module):
      def __init__(self, in_planes, scale=1, c=64):
          super(IFBlock, self).__init__()
          self.scale = scale
          self.conv0 = nn.Sequential(
              conv(in_planes, c, 3, 2, 1),
              )
          self.convblock = nn.Sequential(
              conv(c, c),
              conv(c, c),
              conv(c, c),
              conv(c, c),
              conv(c, c),
              conv(c, c),
              conv(c, c),
              conv(c, c),
          )
          self.conv1 = nn.Conv2d(c, 4, 3, 1, 1)
  
      def forward(self, x):
          if self.scale != 1:
              x = F.interpolate(x, scale_factor= 1. / self.scale, mode="bilinear", align_corners=False)
          x = self.conv0(x)
          x = self.convblock(x) + x
          x = self.conv1(x)
          flow = x
          if self.scale != 1:
              flow = F.interpolate(flow, scale_factor= self.scale, mode="bilinear", align_corners=False)
          return flow
      
  class IFNet(nn.Module):
      def __init__(self):
          super(IFNet, self).__init__()
          self.block0 = IFBlock(6, scale=4, c=360)
          self.block1 = IFBlock(10, scale=2, c=225)
          self.block2 = IFBlock(10, scale=1, c=135)
  
      def forward(self, x):
          flow0 = self.block0(x)
          F1 = flow0
          F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
          warped_img0 = warp(x[:, :3], F1_large[:, :2])
          warped_img1 = warp(x[:, 3:], F1_large[:, 2:4])
          flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1))
          F2 = (flow0 + flow1)
          F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
          warped_img0 = warp(x[:, :3], F2_large[:, :2])
          warped_img1 = warp(x[:, 3:], F2_large[:, 2:4])
          flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1))
          F3 = (flow0 + flow1 + flow2)
          return F3, [F1, F2, F3]
  