code: def test_get_feature_names_out(df_na):
      original_features = df_na.columns.to_list()
      input_features = ["Age", "Marks"]
  
      # when indicators is false, we've got the generic check.
      # We need to test only when true
      tr = Winsorizer(
          tail="left", add_indicators=True, missing_values="ignore"
      )
      tr.fit(df_na)
  
      out = [f + "_left" for f in input_features]
      assert tr.get_feature_names_out() == original_features + out
      assert (
          tr.get_feature_names_out(original_features)
          == original_features + out
      )
  
      tr = Winsorizer(
          tail="right", add_indicators=True, missing_values="ignore"
      )
      tr.fit(df_na)
  
      out = [f + "_right" for f in input_features]
      assert tr.get_feature_names_out() == original_features + out
      assert (
          tr.get_feature_names_out(original_features)
          == original_features + out
      )
  
      tr = Winsorizer(
          tail="both", add_indicators=True, missing_values="ignore"
      )
      tr.fit(df_na)
  
      out = ["Age_left", "Age_right", "Marks_left", "Marks_right"]
      assert tr.get_feature_names_out() == original_features + out
      assert (
          tr.get_feature_names_out(original_features)
          == original_features + out
      )
  
imports: import math
  import numpy
  import pandas
  import pytest
  from feature_engine.outliers import Winsorizer
label_desc: Use image node_id 23 for calling a global function with example usage: test_get_feature_names_out(df_na) without return types