code: def __init__(self):
      super(Net, self).__init__()
      self.conv1 = nn.Conv2d(1, 32, 3, 1)
      self.conv2 = nn.Conv2d(32, 64, 3, 1)
      self.dropout1 = nn.Dropout2d(0.25)
      self.dropout2 = nn.Dropout2d(0.5)
      self.fc1 = nn.Linear(9216, 128)
      self.fc2 = nn.Linear(128, 10)
  
file_path: auptimizer/Examples/compression/utility_functions/mnist.py
imports: from __future__ import print_function
  import argparse
  import os
  import sys
  import time
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torchvision import datasets, transforms
  from torch.optim.lr_scheduler import StepLR
  import aup
label_desc: Use image node_id 1 to create a new Net object from inherited base classes: nn with example: obj = Net()