code: def preprocessed_data(img_rows=28, img_cols=28, num_classes=10):
      """Get data for mnist training and evaluation."""
      (x_train, y_train), (
          x_test,
          y_test,
      ) = tf.keras.datasets.mnist.load_data()
  
      if tf.keras.backend.image_data_format() == "channels_first":
          x_train = x_train.reshape(
              x_train.shape[0], 1, img_rows, img_cols
          )
          x_test = x_test.reshape(
              x_test.shape[0], 1, img_rows, img_cols
          )
      else:
          x_train = x_train.reshape(
              x_train.shape[0], img_rows, img_cols, 1
          )
          x_test = x_test.reshape(
              x_test.shape[0], img_rows, img_cols, 1
          )
  
      x_train = x_train.astype("float32")
      x_test = x_test.astype("float32")
      x_train /= 255
      x_test /= 255
  
      # convert class vectors to binary class matrices
      y_train = tf.keras.utils.to_categorical(y_train, num_classes)
      y_test = tf.keras.utils.to_categorical(y_test, num_classes)
  
      return x_train, y_train, x_test, y_test
  
file_path: model-optimization/tensorflow_model_optimization/python/core/keras/testing/test_utils_mnist.py
imports: import numpy
  import tensorflow
  from tensorflow import keras
label_desc: Use image node_id 5 for calling a global function with example usage: preprocessed_data(img_rows, img_cols, num_classes) and returns: x_train,  y_train,  x_test,  y_test