code: def test_helper(self):
      class SuperResolutionNet(nn.Module):
          def __init__(self, upscale_factor, inplace=False):
              super().__init__()
  
              self.relu = nn.ReLU(inplace=inplace)
              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)
  
              self._initialize_weights()
  
          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
  
          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)
  
      torch_model = SuperResolutionNet(upscale_factor=3)
  
      fake_input = torch.randn(1, 1, 224, 224, requires_grad=True)
  
      # use ModelHelper to create a C2 net
      helper = ModelHelper(name="test_model")
      start = helper.Sigmoid(["the_input"])
      # Embed the ONNX-converted pytorch net inside it
      (toutput,) = PyTorchModule(
          helper, torch_model, (fake_input,), [start]
      )
      output = helper.Sigmoid(toutput)
  
      workspace.RunNetOnce(helper.InitProto())
      workspace.FeedBlob("the_input", fake_input.data.numpy())
      # print([ k for k in workspace.blobs ])
      workspace.RunNetOnce(helper.Proto())
      c2_out = workspace.FetchBlob(str(output))
  
      torch_out = torch.sigmoid(torch_model(torch.sigmoid(fake_input)))
  
      np.testing.assert_almost_equal(
          torch_out.data.cpu().numpy(), c2_out, decimal=3
      )
  
file_path: pytorch/test/onnx_caffe2/test_pytorch_helper.py
imports: import unittest
  import numpy
  import pytorch_test_common
  import torch.nn.init
  import torch.onnx
  from caffe2.python.core import workspace
  from caffe2.python.model_helper import ModelHelper
  from pytorch_helper import PyTorchModule
  from torch import nn
  from torch.testing._internal import common_utils
  from torch.testing._internal.common_utils import skipIfNoLapack
label_desc: Use image node_id 1 for calling the TestCaffe2Backend obj's underlying member method code with example usage: obj.test_helper() and returns: x