code: def test_mad_capping_right_tail_with_fold_1(df_normal_dist):
      # test case 1: median and mad, right tail
      transformer = Winsorizer(
          capping_method="mad", tail="right", fold=1
      )
      X = transformer.fit_transform(df_normal_dist)
  
      # expected output
      df_transf = df_normal_dist.copy()
      df_transf["var"] = df_transf["var"].clip(
          upper=0.10995521088494983
      )
  
      # test init params
      assert transformer.capping_method == "mad"
      assert transformer.tail == "right"
      assert transformer.fold == 1
      # test fit attr
      assert math.isclose(
          transformer.right_tail_caps_["var"], 0.10995521088494983
      )
      assert transformer.left_tail_caps_ == {}
      assert transformer.n_features_in_ == 1
      # test transform outputs
      pd.testing.assert_frame_equal(X, df_transf)
      assert math.isclose(X["var"].max(), 0.10995521088494983)
      assert math.isclose(df_transf["var"].max(), 0.10995521088494983)
  
imports: import math
  import numpy
  import pandas
  import pytest
  from feature_engine.outliers import Winsorizer
label_desc: Use image node_id 9 for calling a global function with example usage: test_mad_capping_right_tail_with_fold_1(df_normal_dist) without return types