code: def load_imdb():
      """Load IMDB movie reviews data."""
      (train_data, train_labels), (
          test_data,
          test_labels,
      ) = tf.keras.datasets.imdb.load_data(num_words=max_features)
  
      train_data = sequence.pad_sequences(
          train_data, maxlen=maxlen
      ).astype("float32")
      test_data = sequence.pad_sequences(
          test_data, maxlen=maxlen
      ).astype("float32")
      return train_data, train_labels, test_data, test_labels
  
file_path: privacy/research/GDP_2019/imdb_tutorial.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl import app
  from absl import flags
  from keras.preprocessing import sequence
  import numpy
  import tensorflow
  from tensorflow import estimator
  from tensorflow.compat.v1 import estimator
  from tensorflow_privacy.privacy.analysis.gdp_accountant import compute_eps_poisson
  from tensorflow_privacy.privacy.analysis.gdp_accountant import compute_mu_poisson
  from tensorflow_privacy.privacy.optimizers import dp_optimizer
label_desc: Use image node_id 2 for calling a global function with example usage: load_imdb() and returns: train_data,  train_labels,  test_data,  test_labels