code: def __init__(self):
      super(Net, self).__init__()
      self.conv_1 = nn.Conv2d(
          in_channels=1, out_channels=4, kernel_size=5, stride=1
      )
      self.conv_2 = nn.Conv2d(
          in_channels=4, out_channels=10, kernel_size=5, stride=1
      )
      self.fc_1 = nn.Linear(in_features=4 * 4 * 10, out_features=100)
      self.fc_2 = nn.Linear(in_features=100, out_features=10)
  
file_path: adversarial-robustness-toolbox/examples/get_started_pytorch.py
imports: import torch.nn
  import torch.nn.functional
  import torch.optim
  import numpy
  from art.attacks.evasion import FastGradientMethod
  from art.estimators.classification import PyTorchClassifier
  from art.utils import load_mnist
label_desc: Use image node_id 1 to create a new Net object from inherited base classes: nn with example: obj = Net()