code: def build_mnist_data(num_classes):
      (x_train, y_train), (
          x_test,
          y_test,
      ) = keras.datasets.mnist.load_data()
  
      # Scale images to the [0, 1] range
      x_train = x_train.astype("float32") / 255
      x_test = x_test.astype("float32") / 255
      # Make sure images have shape (28, 28, 1)
      x_train = np.expand_dims(x_train, -1)
      x_test = np.expand_dims(x_test, -1)
  
      # convert class vectors to binary class matrices
      y_train = keras.utils.to_categorical(y_train, num_classes)
      y_test = keras.utils.to_categorical(y_test, num_classes)
  
      print("x_train shape:", x_train.shape)
      print(x_train.shape[0], "train samples")
      print(x_test.shape[0], "test samples")
  
      return x_train, y_train, x_test, y_test
  
file_path: keras/integration_tests/numerical_test.py
imports: import keras
  import numpy
  from tensorflow import keras
label_desc: Use image node_id 1 for calling a global function with example usage: build_mnist_data(num_classes) and returns: x_train,  y_train,  x_test,  y_test