code: def test_ignores_na_in_input_df(df_na):
      # test case 4: dataset contains na and transformer is asked to ignore them
      transformer = ArbitraryOutlierCapper(
          max_capping_dict=None,
          min_capping_dict={"Age": 20},
          missing_values="ignore",
      )
      X = transformer.fit_transform(df_na)
  
      # expected output
      df_transf = df_na.copy()
      df_transf["Age"] = np.where(
          df_transf["Age"] < 20, 20, df_transf["Age"]
      )
  
      # test fit params
      assert transformer.max_capping_dict is None
      assert transformer.min_capping_dict == {"Age": 20}
      assert transformer.n_features_in_ == 6
      # test transform output
      pd.testing.assert_frame_equal(X, df_transf)
      assert X["Age"].min() >= 20
      assert df_na["Age"].min() < 20
  
file_path: feature_engine/tests/test_outliers/test_arbitrary_capper.py
imports: import numpy
  import pandas
  import pytest
  from feature_engine.outliers import ArbitraryOutlierCapper
label_desc: Use image node_id 4 for calling a global function with example usage: test_ignores_na_in_input_df(df_na) without return types