code: def load_mnist():
      """Loads MNIST and preprocesses to combine training and validation data."""
      train, test = tf.keras.datasets.mnist.load_data()
      train_data, train_labels = train
      test_data, test_labels = test
  
      train_data = np.array(train_data, dtype=np.float32) / 255
      test_data = np.array(test_data, dtype=np.float32) / 255
  
      train_labels = np.array(train_labels, dtype=np.int32)
      test_labels = np.array(test_labels, dtype=np.int32)
  
      assert train_data.min() == 0.0
      assert train_data.max() == 1.0
      assert test_data.min() == 0.0
      assert test_data.max() == 1.0
      assert train_labels.ndim == 1
      assert test_labels.ndim == 1
  
      return train_data, train_labels, test_data, test_labels
  
file_path: privacy/tutorials/mnist_dpsgd_tutorial_vectorized.py
imports: from absl import app
  from absl import flags
  from absl import logging
  import dp_accounting
  import numpy
  import tensorflow
  from tensorflow import estimator
  from tensorflow.compat.v1 import estimator
  from tensorflow_privacy.privacy.optimizers import dp_optimizer_vectorized
label_desc: Use image node_id 3 for calling a global function with example usage: load_mnist() and returns: train_data,  train_labels,  test_data,  test_labels