code: def test_mad_capping_both_tails_with_fold_2(df_normal_dist):
      # test case 2: mean and std, both tails, different fold value
      transformer = Winsorizer(
          capping_method="mad", tail="both", fold=2
      )
      X = transformer.fit_transform(df_normal_dist)
  
      # expected output
      df_transf = df_normal_dist.copy()
      df_transf["var"] = df_transf["var"].clip(
          -0.1916815859385002, 0.21050080982609987
      )
  
      # test fit params
      assert math.isclose(
          transformer.right_tail_caps_["var"], 0.21050080982609987
      )
      assert math.isclose(
          transformer.left_tail_caps_["var"], -0.1916815859385002
      )
      # test transform output
      pd.testing.assert_frame_equal(X, df_transf)
      assert math.isclose(X["var"].max(), 0.21050080982609987)
      assert math.isclose(X["var"].min(), -0.1916815859385002)
      assert math.isclose(df_transf["var"].max(), 0.21050080982609987)
      assert math.isclose(df_transf["var"].min(), -0.1916815859385002)
  
imports: import math
  import numpy
  import pandas
  import pytest
  from feature_engine.outliers import Winsorizer
label_desc: Use image node_id 10 for calling a global function with example usage: test_mad_capping_both_tails_with_fold_2(df_normal_dist) without return types