code: def data():
      (X_train, y_train), (X_test, y_test) = mnist.load_data()
      X_train = X_train.reshape(60000, 784)
      X_test = X_test.reshape(10000, 784)
      X_train = X_train.astype("float32")
      X_test = X_test.astype("float32")
      X_train /= 255
      X_test /= 255
      nb_classes = 10
      Y_train = np_utils.to_categorical(y_train, nb_classes)
      Y_test = np_utils.to_categorical(y_test, nb_classes)
      return X_train, Y_train, X_test, Y_test
  
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 1 for calling a global function with example usage: data() and returns: X_train,  Y_train,  X_test,  Y_test