code: def cnn(opt):
      nn = Sequential()
  
      nn.add(Flatten())
      nn.add(Dense(opt["Dense.0"]))
      nn.add(Activation("relu"))
      nn.add(Dropout(opt["Dropout.0"]))
      nn.add(Dense(10))
      nn.add(Activation("softmax"))
  
      nn.compile(
          optimizer="adam",
          loss="categorical_crossentropy",
          metrics=["accuracy"],
      )
      nn.fit(X_train, y_train, epochs=5, batch_size=256)
  
      _, score = nn.evaluate(x=X_test, y=y_test)
  
      return score
  
file_path: Hyperactive/examples/optimization_applications/transfer_learning.py
imports: import numpy
  from keras.models import Sequential
  from keras import applications
  from keras.layers import Dense, Flatten, Dropout, Activation
  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