code: def test_label_encoder_feature_generator(
      generator_helper, data_helper
  ):
      # Given
      input_data = data_helper.generate_multi_feature_standard()
  
      generator = LabelEncoderFeatureGenerator()
  
      expected_feature_metadata_in_full = {
          ("category", ()): ["cat"],
      }
      expected_feature_metadata_full = {("int", ()): ["cat"]}
  
      expected_output_data_cat_val = [0, 1, 0, 3, 3, 3, 2, -1, -1]
  
      # 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 output_data["cat"].dtype == np.int8
      assert (
          list(output_data["cat"].values)
          == expected_output_data_cat_val
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_label_encoder_feature_generator(generator_helper, data_helper) without return types