code: def test_super_resolution(self):
      class Net(nn.Module):
          def __init__(self, upscale_factor):
              super(Net, self).__init__()
  
              self.relu = nn.ReLU()
              self.conv1 = nn.Conv2d(1, 64, (5, 5), (1, 1), (2, 2))
              self.conv2 = nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1))
              self.conv3 = nn.Conv2d(64, 32, (3, 3), (1, 1), (1, 1))
              self.conv4 = nn.Conv2d(
                  32, upscale_factor**2, (3, 3), (1, 1), (1, 1)
              )
              self.pixel_shuffle = nn.PixelShuffle(upscale_factor)
  
          def forward(self, x):
              x = self.relu(self.conv1(x))
              x = self.relu(self.conv2(x))
              x = self.relu(self.conv3(x))
              x = self.pixel_shuffle(self.conv4(x))
              return x
  
      net = Net(upscale_factor=4)
      self.checkExportImport(net, (torch.rand(5, 1, 32, 32),))
  
imports: import unittest
  import torch
  import torch.nn.functional
  import torchvision
  import nni.nas.nn.pytorch.layers
  from nni.nas.nn.pytorch import ParametrizedModule
  from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
label_desc: Use image node_id 6 for calling the TestConvert obj's underlying member method code with example usage: obj.test_super_resolution() and returns: x