code: def test_one_hot_encoder_feature_generator(
      generator_helper, data_helper
  ):
      # Given
      input_data = data_helper.generate_multi_feature_standard()
  
      generator = OneHotEncoderFeatureGenerator()
  
      expected_feature_metadata_in_full = {
          ("category", ()): ["cat"],
          ("int", ()): ["int"],
      }
      expected_feature_metadata_full = {
          ("int", ("bool", "sparse")): [
              "_ohe_0",
              "_ohe_1",
              "_ohe_2",
              "_ohe_3",
              "_ohe_4",
              "_ohe_5",
              "_ohe_6",
              "_ohe_7",
              "_ohe_8",
              "_ohe_9",
              "_ohe_10",
          ]
      }
  
      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) == 11
      assert output_data["_ohe_1"].dtype.subtype == np.uint8
      assert (
          list(output_data["_ohe_1"].values)
          == expected_output_data_int_0_val
      )
  
imports: import numpy
  from autogluon.features.generators import OneHotEncoderFeatureGenerator
label_desc: Use image node_id 1 for calling a global function with example usage: test_one_hot_encoder_feature_generator(generator_helper, data_helper) without return types