code: def prepare_dataset():
      """Prepare the dataset."""
  
      max_features = 20000
      maxlen = 100  # cut texts after this number of words
  
      print("Loading data...")
      (x_train, y_train), (
          x_test,
          y_test,
      ) = tf.keras.datasets.imdb.load_data(num_words=max_features)
      print(len(x_train), "train sequences")
      print(len(x_test), "test sequences")
  
      print("Pad sequences (samples x time)")
      x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
      x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
  
      return x_train, y_train, x_test, y_test
  
imports: from __future__ import print_function
  import tensorflow
  from tensorflow_model_optimization.python.core.clustering.keras import cluster
  from tensorflow_model_optimization.python.core.clustering.keras import cluster_config
label_desc: Use image node_id 1 for calling a global function with example usage: prepare_dataset() and returns: x_train,  y_train,  x_test,  y_test