code: def test_transformer_ignores_na_in_df(df_na):
      # test case 7: dataset contains na and transformer is asked to ignore them
      transformer = Winsorizer(
          capping_method="gaussian",
          tail="right",
          fold=1,
          variables=["Age", "Marks"],
          missing_values="ignore",
      )
      X = transformer.fit_transform(df_na)
  
      # expected output
      df_transf = df_na.copy()
      df_transf["Age"] = df_transf["Age"].clip(upper=38.04494616731882)
      df_transf["Marks"] = df_transf["Marks"].clip(
          upper=0.8784116651786605
      )
  
      # test fit params
      assert math.isclose(
          transformer.right_tail_caps_["Age"], 38.04494616731882
      )
      assert math.isclose(
          transformer.right_tail_caps_["Marks"], 0.8784116651786605
      )
      assert transformer.left_tail_caps_ == {}
      assert transformer.n_features_in_ == 6
      # test transform output
      pd.testing.assert_frame_equal(X, df_transf)
      assert math.isclose(X["Age"].max(), 38.04494616731882)
      assert math.isclose(X["Age"].max(), 38.04494616731882)
      assert math.isclose(X["Marks"].max(), 0.8784116651786605)
      assert math.isclose(df_transf["Marks"].max(), 0.8784116651786605)
  
imports: import math
  import numpy
  import pandas
  import pytest
  from feature_engine.outliers import Winsorizer
label_desc: Use image node_id 14 for calling a global function with example usage: test_transformer_ignores_na_in_df(df_na) without return types