code: def test_feature_duplicator():
      input_df = pd.DataFrame(
          {
              "a": [1, 2, 3],
              "b": [4, 5, 6],
          }
      )
  
      expected1 = pd.DataFrame(
          {
              "a": [1, 2, 3],
              "b": [4, 5, 6],
              "prefix__a": [1, 2, 3],
          }
      )
  
      expected2 = pd.DataFrame(
          {
              "a": [1, 2, 3],
              "b": [4, 5, 6],
              "a__suffix": [1, 2, 3],
              "b__suffix": [4, 5, 6],
          }
      )
  
      expected3 = pd.DataFrame(
          {
              "a": [1, 2, 3],
              "b": [4, 5, 6],
              "c": [4, 5, 6],
          }
      )
  
      assert expected1.equals(
          feature_duplicator(
              input_df.copy(),
              columns_to_duplicate=["a"],
              prefix="prefix__",
          )[1]
      )
      assert expected2.equals(
          feature_duplicator(
              input_df.copy(),
              columns_to_duplicate=["a", "b"],
              suffix="__suffix",
          )[1]
      )
      assert expected3.equals(
          feature_duplicator(
              input_df.copy(),
              columns_mapping={"b": "c"},
          )[1]
      )
  
imports: import pandas
  from fklearn.training.transformation import capper
  from fklearn.preprocessing.schema import feature_duplicator
label_desc: Use image node_id 1 for calling a global function with example usage: test_feature_duplicator() without return types