code: def test_pipeline_feature_generator_dummy(
      generator_helper, data_helper
  ):
      input_data = data_helper.generate_multi_feature_full()
  
      input_data_transform = data_helper.generate_empty()
  
      generator = PipelineFeatureGenerator(
          generators=[
              [
                  IdentityFeatureGenerator(
                      infer_features_in_args=dict(
                          valid_raw_types=["unknown_raw_type"]
                      )
                  ),
              ]
          ]
      )
  
      expected_feature_metadata_in_full = {}
      expected_feature_metadata_full = {("int", ()): ["__dummy__"]}
  
      # 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 list(output_data["__dummy__"].values) == [
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
      ]
  
      assert list(
          generator.transform(input_data_transform)["__dummy__"].values
      ) == [0, 0, 0, 0, 0, 0, 0, 0, 0]
      assert list(
          generator.transform(input_data_transform.head(5))[
              "__dummy__"
          ].values
      ) == [0, 0, 0, 0, 0]
  
imports: import numpy
  from sklearn.feature_extraction.text import CountVectorizer
  from autogluon.common.features.types import R_CATEGORY, R_FLOAT, R_INT
  from autogluon.features.generators import CategoryFeatureGenerator, DatetimeFeatureGenerator, IdentityFeatureGenerator, PipelineFeatureGenerator, TextNgramFeatureGenerator, TextSpecialFeatureGenerator
label_desc: Use image node_id 2 for calling a global function with example usage: test_pipeline_feature_generator_dummy(generator_helper, data_helper) without return types