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: from __future__ import annotations
  import time
  from typing import Callable
  import torch
  import torch.nn.functional
  from torch.optim import Optimizer, SGD
  from torch.utils.data import DataLoader
  from torchvision import transforms
  from torchvision.datasets import MNIST
  import nni
  from nni.compression.quantization import QATQuantizer
  from nni.compression.utils import TorchEvaluator
  from nni.common.types import SCHEDULER
label_desc: Use image node_id 2 for calling the NaiveModel obj's underlying member method code with example usage: obj.forward(x) and returns: F