code: def __init__(self):
      super(Net, self).__init__()
      self.conv1 = Conv2D(
          64, 8, strides=(2, 2), activation="relu", padding="same"
      )
      self.conv2 = Conv2D(
          128, 6, strides=(2, 2), activation="relu", padding="valid"
      )
      self.conv3 = Conv2D(
          128, 5, strides=(1, 1), activation="relu", padding="valid"
      )
      self.dropout = Dropout(0.25)
      self.flatten = Flatten()
      self.dense1 = Dense(128, activation="relu")
      self.dense2 = Dense(10)
  
file_path: cleverhans/tutorials/tf2/mnist_tutorial.py
imports: import numpy
  import tensorflow
  import tensorflow_datasets
  from absl import app, flags
  from easydict import EasyDict
  from tensorflow.keras import Model
  from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D
  from cleverhans.tf2.attacks.projected_gradient_descent import projected_gradient_descent
  from cleverhans.tf2.attacks.fast_gradient_method import fast_gradient_method
label_desc: Use image node_id 1 to create a new Net object from inherited base classes: Model with example: obj = Net()