code: def __init__(self):
      super().__init__()
      self.conv1 = torch.nn.Conv2d(1, 5, 5, 1)
      self.bn1 = torch.nn.BatchNorm2d(5)
      self.conv2 = torch.nn.Conv2d(5, 10, 5, 1)
      self.bn2 = torch.nn.BatchNorm2d(10)
      self.fc1 = torch.nn.Linear(4 * 4 * 10, 100)
      self.fc2 = torch.nn.Linear(100, 10)
  
imports: from unittest import TestCase, main
  import numpy
  import torch
  import torch.nn.functional
  import schema
  from aup.compression.torch.pruning import *
  from aup.compression.torch.quantization import *
  import math
label_desc: Use image node_id 1 to create a new TorchModel object from inherited base classes: torch.nn with example: obj = TorchModel()