code: def test_preprocessing_adapt_with_cat_to_int_and_norm():
      x = np.array([["a", 5], ["b", 6]]).astype(str)
      y = np.array([[1, 2], [3, 4]]).astype(str)
      dataset = tf.data.Dataset.from_tensor_slices((x, y)).batch(32)
      model = keras.models.Sequential()
      model.add(keras.Input(shape=(2,), dtype=tf.string))
      model.add(keras_layers.MultiCategoryEncoding(["int", "none"]))
      model.add(preprocessing.Normalization(axis=-1))
  
      tuner_module.AutoTuner.adapt(model, dataset)
  
imports: from unittest import mock
  import numpy
  import tensorflow
  from tensorflow import keras
  from tensorflow.keras.layers.experimental import preprocessing
  import autokeras
  from autokeras import keras_layers
  from autokeras import test_utils
  from autokeras.engine import tuner
  from autokeras.tuners import greedy
label_desc: Use image node_id 9 for calling a global function with example usage: test_preprocessing_adapt_with_cat_to_int_and_norm() without return types