code: def test_model_save_load_output_same(tmp_path):
      x_train = np.array(
          [["a", "ab", 2.1], ["b", "bc", 1.0], ["a", "bc", "nan"]]
      )
      layer = layer_module.MultiCategoryEncoding(
          encoding=[
              layer_module.INT,
              layer_module.INT,
              layer_module.NONE,
          ]
      )
      layer.adapt(tf.data.Dataset.from_tensor_slices(x_train).batch(32))
  
      model = keras.Sequential(
          [keras.Input(shape=(3,), dtype=tf.string), layer]
      )
      model.save(os.path.join(tmp_path, "model"))
      model2 = keras.models.load_model(os.path.join(tmp_path, "model"))
  
      assert np.array_equal(
          model.predict(x_train), model2.predict(x_train)
      )
  
file_path: autokeras/autokeras/keras_layers_test.py
imports: import os
  import numpy
  import tensorflow
  from tensorflow import keras
  from autokeras import keras_layers
label_desc: Use image node_id 2 for calling a global function with example usage: test_model_save_load_output_same(tmp_path) without return types