code: def test_multi_cat_encode_strings_correctly(tmp_path):
      x_train = np.array(
          [["a", "ab", 2.1], ["b", "bc", 1.0], ["a", "bc", "nan"]]
      )
      layer = layer_module.MultiCategoryEncoding(
          [layer_module.INT, layer_module.INT, layer_module.NONE]
      )
      dataset = tf.data.Dataset.from_tensor_slices(x_train).batch(32)
  
      layer.adapt(tf.data.Dataset.from_tensor_slices(x_train).batch(32))
  
      for data in dataset:
          result = layer(data)
  
      assert result[0][0] == result[2][0]
      assert result[0][0] != result[1][0]
      assert result[0][1] != result[1][1]
      assert result[0][1] != result[2][1]
      assert result[2][2] == 0
      assert result.dtype == tf.float32
  
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 1 for calling a global function with example usage: test_multi_cat_encode_strings_correctly(tmp_path) without return types