code: def cnn(opt):
      model = keras.models.clone_model(model_pretrained)
  
      model.add(Flatten())
      model.add(Dense(opt["neurons.0"]))
      model.add(Activation("relu"))
      model.add(Dropout(0.5))
      model.add(Dense(10))
      model.add(Activation("softmax"))
  
      model.compile(
          optimizer="adam",
          loss="categorical_crossentropy",
          metrics=["accuracy"],
      )
      model.fit(X_train, y_train, epochs=5, batch_size=500)
  
      model.summary()
  
      _, score = model.evaluate(x=X_test, y=y_test)
  
      return score
  
file_path: Hyperactive/examples/optimization_applications/pretrained_nas.py
imports: import numpy
  import keras
  from keras.models import Sequential
  from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Activation, Dropout
  from keras.datasets import cifar10
  from keras.utils import to_categorical
  from hyperactive import Hyperactive
label_desc: Use image node_id 1 for calling a global function with example usage: cnn(opt) and returns: score