code: def make_basic_cnn(
      nb_filters=64, nb_classes=10, input_shape=(None, 28, 28, 1)
  ):
      layers = [
          Conv2D(nb_filters, (8, 8), (2, 2), "SAME"),
          ReLU(),
          Conv2D(nb_filters * 2, (6, 6), (2, 2), "VALID"),
          ReLU(),
          Conv2D(nb_filters * 2, (5, 5), (1, 1), "VALID"),
          ReLU(),
          Flatten(),
          Linear(nb_classes),
          Softmax(),
      ]
  
      model = MLP(nb_classes, layers, input_shape)
      return model
  
file_path: cleverhans/cleverhans_v3.1.0/examples/multigpu_advtrain/make_model.py
imports: from model import Conv2D, ReLU, Flatten, Linear, Softmax, MLP
  from model import MLPnGPU
  from model import Conv2DnGPU
  from model import LinearnGPU
  from model import MaxPool
  from resnet_tf import ResNetTF
label_desc: Use image node_id 1 for calling a global function with example usage: make_basic_cnn(nb_filters, nb_classes, input_shape) and returns: model