text: import torch.nn as nn
  import torch.nn.init as init
  
  
  class Net(nn.Module):
      def __init__(self, upscale_factor):
          super(Net, self).__init__()
  
          self.relu = nn.ReLU()
          self.conv1 = nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2)
          self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
          self.conv3 = nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1)
          self.conv4 = nn.Conv2d(32, upscale_factor ** 2, kernel_size=3, stride=1, padding=1)
          self.pixel_shuffle = nn.PixelShuffle(upscale_factor)
  
          self._initialize_weights()
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.relu(x)
          x = self.conv2(x)
          x = self.relu(x)
          x = self.conv3(x)
          x = self.relu(x)
          x = self.conv4(x)
          x = self.pixel_shuffle(x)
          return x
  
      def _initialize_weights(self):
          init.orthogonal(self.conv1.weight, init.calculate_gain('relu'))
          init.orthogonal(self.conv2.weight, init.calculate_gain('relu'))
          init.orthogonal(self.conv3.weight, init.calculate_gain('relu'))
          init.orthogonal(self.conv4.weight)
  