code: def model_multi_line_arguments(X_train, Y_train, X_test, Y_test):
      inputs = Input(shape=(784,))
  
      x = Dense({{choice([20, 30, 40])}}, activation="relu")(inputs)
      x = Dense(64, activation="relu")(x)
      predictions = Dense(10, activation="softmax")(x)
      model = Model(inputs=inputs, outputs=predictions)
  
      model.compile(
          loss="categorical_crossentropy",
          optimizer="adam",
          metrics=["accuracy"],
      )
  
      model.fit(
          X_train,
          Y_train,
          batch_size={{choice([64, 128])}},
          epochs=1,
          verbose=2,
          validation_data=(X_test, Y_test),
      )
      score, acc = model.evaluate(X_test, Y_test, verbose=0)
      print("Test accuracy:", acc)
      return {"loss": -acc, "status": STATUS_OK, "model": model}
  
file_path: hyperas/tests/test_functional_api.py
imports: from __future__ import print_function
  from hyperopt import Trials, STATUS_OK, tpe
  from hyperas import optim
  from hyperas.distributions import choice
  from keras.models import Model
  from keras.layers import Dense, Input
  from keras.optimizers import RMSprop
  from keras.datasets import mnist
  from keras.utils import np_utils
label_desc: Use image node_id 3 for calling a global function with example usage: model_multi_line_arguments(X_train, Y_train, X_test, Y_test) and returns: dict