code: def __init__(self):
      super(Net, self).__init__()
  
      self.conv1 = torch.nn.Sequential(
          torch.nn.Conv2d(1, 32, 3, 1, 1),
          torch.nn.ReLU(),
          torch.nn.MaxPool2d(2),
      )
      self.conv2 = torch.nn.Sequential(
          torch.nn.Conv2d(32, 64, 3, 1, 1),
          torch.nn.ReLU(),
          torch.nn.MaxPool2d(2),
      )
      self.conv3 = torch.nn.Sequential(
          torch.nn.Conv2d(64, 64, 3, 1, 1),
          torch.nn.ReLU(),
          torch.nn.MaxPool2d(2),
      )
      self.dense = torch.nn.Sequential(
          torch.nn.Linear(64 * 3 * 3, 128),
          torch.nn.ReLU(),
          torch.nn.Linear(128, 10),
      )
  
file_path: AdvBox/tutorials/mnist_model_pytorch.py
imports: from __future__ import print_function
  from __future__ import division
  from builtins import range
  from past.utils import old_div
  import os
  import torch
  import torchvision
  from torch.autograd import Variable
  import torch.utils.data.dataloader
label_desc: Use image node_id 1 to create a new Net object from inherited base classes: torch.nn with example: obj = Net()