code: def test_datetime_feature_generator_advanced(
      generator_helper, data_helper
  ):
      # Given
      input_data = data_helper.generate_datetime_as_object_feature_advanced().to_frame(
          name="datetime_as_object"
      )
  
      generator = DatetimeFeatureGenerator()
  
      expected_feature_metadata_in_full = {
          ("object", ("datetime_as_object",)): ["datetime_as_object"],
      }
  
      expected_feature_metadata_full = {
          ("int", ("datetime_as_int",)): [
              "datetime_as_object",
              "datetime_as_object.year",
              "datetime_as_object.month",
              "datetime_as_object.day",
              "datetime_as_object.dayofweek",
          ]
      }
  
      expected_output_data_feat_datetime = [
          1533140820000000000,
          1600067037034500096,
          1600067037034500096,
          1628543228659000000,
          1628543295541000000,
          1610040803938000000,
      ]
  
      # 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,
      )
  
      assert expected_output_data_feat_datetime == list(
          output_data["datetime_as_object"].values
      )
  
label_desc: Use image node_id 2 for calling a global function with example usage: test_datetime_feature_generator_advanced(generator_helper, data_helper) without return types