code: def test_automatically_find_variables_and_gaussian_imputation_on_right_tail(
      df_na,
  ):
      # set up transformer
      imputer = EndTailImputer(
          imputation_method="gaussian",
          tail="right",
          fold=3,
          variables=None,
      )
      X_transformed = imputer.fit_transform(df_na)
  
      # set up expected output
      X_reference = df_na.copy()
      X_reference["Age"] = X_reference["Age"].fillna(58.94908118478389)
      X_reference["Marks"] = X_reference["Marks"].fillna(
          1.3244261503263175
      )
  
      # test init params
      assert imputer.imputation_method == "gaussian"
      assert imputer.tail == "right"
      assert imputer.fold == 3
      assert imputer.variables is None
      # test fit attr
      assert imputer.variables_ == ["Age", "Marks"]
      assert imputer.n_features_in_ == 6
      imputer.imputer_dict_ = {
          key: round(value, 3)
          for (key, value) in imputer.imputer_dict_.items()
      }
      assert imputer.imputer_dict_ == {
          "Age": 58.949,
          "Marks": 1.324,
      }
      # transform output: indicated vars ==> no NA, not indicated vars with NA
      assert X_transformed[["Age", "Marks"]].isnull().sum().sum() == 0
      assert X_transformed[["City", "Name"]].isnull().sum().sum() > 0
      pd.testing.assert_frame_equal(X_transformed, X_reference)
  
file_path: feature_engine/tests/test_imputation/test_end_tail_imputer.py
imports: import numpy
  import pandas
  import pytest
  from feature_engine.imputation import EndTailImputer
label_desc: Use image node_id 1 for calling a global function with example usage: test_automatically_find_variables_and_gaussian_imputation_on_right_tail(df_na) without return types