code: def __init__(self):
      super().__init__()
      self.conv1 = torch.nn.Conv2d(1, 20, 5, 1)
      self.conv2 = torch.nn.Conv2d(20, 50, 5, 1)
      self.fc1 = torch.nn.Linear(4 * 4 * 50, 500)
      self.fc2 = torch.nn.Linear(500, 10)
      self.relu1 = torch.nn.ReLU6()
      self.relu2 = torch.nn.ReLU6()
      self.relu3 = torch.nn.ReLU6()
      self.max_pool1 = torch.nn.MaxPool2d(2, 2)
      self.max_pool2 = torch.nn.MaxPool2d(2, 2)
      self.batchnorm1 = torch.nn.BatchNorm2d(20)
  
imports: import functools
  from typing import Callable, Union, List, Dict, Tuple
  import torch
  import torch.nn.functional
  from torch.optim import Optimizer
  from torch.utils.data import DataLoader
  from torch import Tensor
  from torchvision import datasets, transforms
  import nni
  from nni.compression.quantization import LsqQuantizer
  from nni.compression.utils import TorchEvaluator
  from nni.common.types import SCHEDULER
label_desc: Use image node_id 1 to create a new Mnist object from inherited base classes: torch.nn with example: obj = Mnist()