text: import functools
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import models.modules.module_util as mutil
  
  
  class ResidualDenseBlock_5C(nn.Module):
      def __init__(self, nf=64, gc=32, bias=True):
          super(ResidualDenseBlock_5C, self).__init__()
          # gc: growth channel, i.e. intermediate channels
          self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
          self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
          self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
          self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
          self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
          self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
  
          # initialization
          mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
  
      def forward(self, x):
          x1 = self.lrelu(self.conv1(x))
          x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
          x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
          x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
          x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
          return x5 * 0.2 + x
  
  
  class RRDB(nn.Module):
      '''Residual in Residual Dense Block'''
  
      def __init__(self, nf, gc=32):
          super(RRDB, self).__init__()
          self.RDB1 = ResidualDenseBlock_5C(nf, gc)
          self.RDB2 = ResidualDenseBlock_5C(nf, gc)
          self.RDB3 = ResidualDenseBlock_5C(nf, gc)
  
      def forward(self, x):
          out = self.RDB1(x)
          out = self.RDB2(out)
          out = self.RDB3(out)
          return out * 0.2 + x
  
  
  class RRDBNet(nn.Module):
      def __init__(self, in_nc, out_nc, nf, nb, gc=32):
          super(RRDBNet, self).__init__()
          RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
  
          self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
          self.RRDB_trunk = mutil.make_layer(RRDB_block_f, nb)
          self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
          #### upsampling
          self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
          self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
          self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
          self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
  
          self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
  
      def forward(self, x):
          fea = self.conv_first(x)
          trunk = self.trunk_conv(self.RRDB_trunk(fea))
          fea = fea + trunk
  
          fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2.0, mode='nearest')))
          # fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2.0, mode='nearest')))
          out = self.conv_last(self.lrelu(self.HRconv(fea)))
  
          return out
  