code: def test_indicators_are_added(df_normal_dist):
      transformer = Winsorizer(
          tail="both",
          capping_method="quantiles",
          fold=0.1,
          add_indicators=True,
      )
      X = transformer.fit_transform(df_normal_dist)
      # test that the number of output variables is correct
      assert X.shape[1] == 3 * df_normal_dist.shape[1]
      assert np.all(
          X.iloc[:, df_normal_dist.shape[1] :].sum(axis=0) > 0
      )
  
      transformer = Winsorizer(
          tail="left",
          capping_method="quantiles",
          fold=0.1,
          add_indicators=True,
      )
      X = transformer.fit_transform(df_normal_dist)
      assert X.shape[1] == 2 * df_normal_dist.shape[1]
      assert np.all(
          X.iloc[:, df_normal_dist.shape[1] :].sum(axis=0) > 0
      )
  
      transformer = Winsorizer(
          tail="right",
          capping_method="quantiles",
          fold=0.1,
          add_indicators=True,
      )
      X = transformer.fit_transform(df_normal_dist)
      assert X.shape[1] == 2 * df_normal_dist.shape[1]
      assert np.all(
          X.iloc[:, df_normal_dist.shape[1] :].sum(axis=0) > 0
      )
  
imports: import math
  import numpy
  import pandas
  import pytest
  from feature_engine.outliers import Winsorizer
label_desc: Use image node_id 11 for calling a global function with example usage: test_indicators_are_added(df_normal_dist) without return types