code: def load_mnist():
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
  
      x_train = x_train.astype("float32") / 255.0
      x_train = x_train.reshape(x_train.shape + (1,))
      x_test = x_test.astype("float32") / 255.0
      x_test = x_test.reshape(x_test.shape + (1,))
  
      return (x_train, y_train), (x_test, y_test)
  
imports: import pickle
  import os
  from keras.datasets import mnist, cifar100, cifar10
  from keras.preprocessing.image import ImageDataGenerator, load_img, save_img, img_to_array
  import pandas
  import numpy
  from os import walk, getcwd
  import h5py
  import scipy
  from glob import glob
  from keras.applications import vgg19
  from keras import backend
  from keras.utils import to_categorical
  import pdb
label_desc: Use image node_id 2 for calling a global function with example usage: load_mnist() and returns: x_train,  y_train,  x_test,  y_test