code: def data():
      nb_classes = 10
      # the data, shuffled and split between train and test sets
      (X_train, y_train), (X_test, y_test) = cifar10.load_data()
      print("X_train shape:", X_train.shape)
      print(X_train.shape[0], "train samples")
      print(X_test.shape[0], "test samples")
  
      # convert class vectors to binary class matrices
      Y_train = np_utils.to_categorical(y_train, nb_classes)
      Y_test = np_utils.to_categorical(y_test, nb_classes)
  
      X_train = X_train.astype("float32")
      X_test = X_test.astype("float32")
      X_train /= 255
      X_test /= 255
  
      # this will do preprocessing and realtime data augmentation
      datagen = ImageDataGenerator(
          featurewise_center=False,  # set input mean to 0 over the dataset
          samplewise_center=False,  # set each sample mean to 0
          featurewise_std_normalization=False,  # divide inputs by std of the dataset
          samplewise_std_normalization=False,  # divide each input by its std
          zca_whitening=False,  # apply ZCA whitening
          rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)
          width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
          height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
          horizontal_flip=True,  # randomly flip images
          vertical_flip=False,
      )  # randomly flip images
  
      # compute quantities required for featurewise normalization
      # (std, mean, and principal components if ZCA whitening is applied)
      datagen.fit(X_train)
  
      return datagen, X_train, Y_train, X_test, Y_test
  
file_path: hyperas/examples/cifar_generator_cnn.py
imports: from __future__ import print_function
  from hyperopt import Trials, STATUS_OK, tpe
  from hyperas import optim
  from hyperas.distributions import uniform
  from keras.models import Sequential
  from keras.layers.core import Dense, Dropout, Activation, Flatten
  from keras.layers.convolutional import Convolution2D, MaxPooling2D
  from keras.optimizers import SGD
  from keras.preprocessing.image import ImageDataGenerator
  from keras.datasets import cifar10
  from keras.utils import np_utils
label_desc: Use image node_id 1 for calling a global function with example usage: data() and returns: datagen,  X_train,  Y_train,  X_test,  Y_test