code: def test_one_hot_encoder_feature_generator_advanced(
      generator_helper, data_helper
  ):
      # Given
      input_data = data_helper.generate_multi_feature_standard()
  
      generator = OneHotEncoderFeatureGenerator(
          max_levels=3, sparse=False, dtype=np.uint16
      )
  
      expected_feature_metadata_in_full = {
          ("category", ()): ["cat"],
          ("int", ()): ["int"],
      }
      # TODO: improve readability of names when max_levels is specified
      expected_feature_metadata_full = {
          ("int", ("bool",)): [
              "_ohe_0",
              "_ohe_1",
              "_ohe_2",
              "_ohe_3",
              "_ohe_4",
              "_ohe_5",
              "_ohe_6",
              "_ohe_7",
          ]
      }
  
      expected_output_data_int_0_val = [0, 1, 0, 0, 0, 0, 0, 1, 0]
  
      # When
      output_data = generator_helper.fit_transform_assert(
          input_data=input_data,
          generator=generator,
          expected_feature_metadata_in_full=expected_feature_metadata_in_full,
          expected_feature_metadata_full=expected_feature_metadata_full,
      )
  
      # Therefore
      assert len(output_data.columns) == 8
      assert output_data["_ohe_0"].dtype == np.uint16
      assert (
          list(output_data["_ohe_0"].values)
          == expected_output_data_int_0_val
      )
  
imports: import numpy
  from autogluon.features.generators import OneHotEncoderFeatureGenerator
label_desc: Use image node_id 2 for calling a global function with example usage: test_one_hot_encoder_feature_generator_advanced(generator_helper, data_helper) without return types