code: def forward(self, x):
      x = self.relu1(self.conv1(x))
      x = self.max_pool1(x)
      x = self.relu2(self.conv2(x))
      x = self.max_pool2(x)
      x = x.view(-1, x.size()[1:].numel())
      x = self.relu3(self.fc1(x))
      x = self.fc2(x)
      return F.log_softmax(x, dim=1)
  
imports: import os
  import torch
  import torch.nn.functional
  from torchvision.models.vgg import vgg16
  from torchvision import datasets, transforms
  import unittest
  from unittest import TestCase, main
  from nni.compression.quantization import QATQuantizer
  from nni.compression.quantization_speedup import ModelSpeedupTensorRT
label_desc: Use image node_id 2 for calling the BackboneModel obj's underlying member method code with example usage: obj.forward(x) and returns: F