code: def test_when_df_cols_are_integers(df_vartypes):
      df = df_vartypes.copy()
      df.columns = [0, 1, 2, 3, 4]
  
      transformer = RelativeFeatures(
          variables=[2, 3],
          reference=[2, 3],
          func=["sub", "add"],
      )
  
      X = transformer.fit_transform(df)
  
      ref = pd.DataFrame.from_dict(
          {
              0: ["tom", "nick", "krish", "jack"],
              1: ["London", "Manchester", "Liverpool", "Bristol"],
              2: [20, 21, 19, 18],
              3: [0.9, 0.8, 0.7, 0.6],
              4: pd.date_range("2020-02-24", periods=4, freq="T"),
              "2_sub_2": [0, 0, 0, 0],
              "3_sub_2": [-19.1, -20.2, -18.3, -17.4],
              "2_sub_3": [19.1, 20.2, 18.3, 17.4],
              "3_sub_3": [0.0, 0.0, 0.0, 0.0],
              "2_add_2": [40, 42, 38, 36],
              "3_add_2": [20.9, 21.8, 19.7, 18.6],
              "2_add_3": [20.9, 21.8, 19.7, 18.6],
              "3_add_3": [1.8, 1.6, 1.4, 1.2],
          }
      )
  
      pd.testing.assert_frame_equal(X, ref)
  
imports: import numpy
  import pandas
  import pytest
  from sklearn.pipeline import Pipeline
  from feature_engine.creation import RelativeFeatures
label_desc: Use image node_id 14 for calling a global function with example usage: test_when_df_cols_are_integers(df_vartypes) without return types