code: def mnist_dataset() -> Tuple[RegressionDataset, RegressionDataset]:
      """Generates (normalized) train and test data for MNIST.
  
      Returns:
        train_dataset: MNIST labeled examples, with unit l2-norm points.
        test_dataset: MNIST labeled examples, with unit l2-norm points.
      """
      train_data, test_data = tf.keras.datasets.mnist.load_data()
      train_points_non_normalized, train_labels = train_data
      test_points_non_normalized, test_labels = test_data
      num_train = train_points_non_normalized.shape[0]
      num_test = test_points_non_normalized.shape[0]
      train_points_non_normalized = train_points_non_normalized.reshape(
          (num_train, -1)
      )
      test_points_non_normalized = test_points_non_normalized.reshape(
          (num_test, -1)
      )
      train_points = preprocessing.normalize(
          train_points_non_normalized
      )
      test_points = preprocessing.normalize(test_points_non_normalized)
      return (
          RegressionDataset(train_points, train_labels, None),
          RegressionDataset(test_points, test_labels, None),
      )
  
file_path: privacy/tensorflow_privacy/privacy/logistic_regression/datasets.py
imports: import dataclasses
  from typing import Tuple, Optional
  import numpy
  from sklearn import preprocessing
  import tensorflow
label_desc: Use image node_id 3 for calling a global function with example usage: mnist_dataset() and returns: RegressionDataset,  RegressionDataset