code: def test_left_tail_capping(df_normal_dist):
      # test case 3: left tail
      transformer = ArbitraryOutlierCapper(
          max_capping_dict=None,
          min_capping_dict={"var": -0.17486039103044},
      )
      X = transformer.fit_transform(df_normal_dist)
  
      # expected output
      df_transf = df_normal_dist.copy()
      df_transf["var"] = np.where(
          df_transf["var"] < -0.17486039103044,
          -0.17486039103044,
          df_transf["var"],
      )
  
      # test init param
      assert transformer.max_capping_dict is None
      assert np.round(
          transformer.min_capping_dict["var"], 3
      ) == np.round(-0.17486039103044, 3)
      # test fit attr
      assert transformer.right_tail_caps_ == {}
      assert np.round(
          transformer.left_tail_caps_["var"], 3
      ) == np.round(-0.17486039103044, 3)
      # test transform output
      pd.testing.assert_frame_equal(X, df_transf)
      assert np.round(X["var"].min(), 3) >= np.round(
          -0.17486039103044, 3
      )
      assert np.round(df_normal_dist["var"].min(), 3) < np.round(
          -0.17486039103044, 3
      )
  
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 3 for calling a global function with example usage: test_left_tail_capping(df_normal_dist) without return types