code: def test_get_feature_names_out(_drop, df_vartypes):
      transformer = RelativeFeatures(
          variables=["Age", "Marks"],
          reference=["Age", "Marks"],
          func=["add", "sub"],
          drop_original=_drop,
      )
      varnames = [
          "Age_add_Age",
          "Marks_add_Age",
          "Age_add_Marks",
          "Marks_add_Marks",
          "Age_sub_Age",
          "Marks_sub_Age",
          "Age_sub_Marks",
          "Marks_sub_Marks",
      ]
      X = transformer.fit_transform(df_vartypes)
      feat_out = list(X.columns)
      assert feat_out == transformer.get_feature_names_out(
          input_features=None
      )
      assert feat_out == transformer.get_feature_names_out(
          input_features=df_vartypes.columns
      )
      assert all([f for f in varnames if f in feat_out])
  
imports: import numpy
  import pandas
  import pytest
  from sklearn.pipeline import Pipeline
  from feature_engine.creation import RelativeFeatures
label_desc: Use image node_id 16 for calling a global function with example usage: test_get_feature_names_out(_drop, df_vartypes) without return types