code: def test_transform_only_selected(self):
      x = pd.DataFrame(
          [
              ["a", "b", "c"],
              ["a", "a", "c"],
              ["b", "a", "c"],
              ["b", "c", "b"],
              ["b", "b", "b"],
              ["a", "b", "a"],
          ],
          columns=["f1", "f2", "f3"],
      )
      y = ["bee", "cat", "dog", "dog", "dog", "dog"]
      wrapper = PolynomialWrapper(
          encoders.LeaveOneOutEncoder(cols=["f2"])
      )
  
      # combination fit() + transform()
      wrapper.fit(x, y)
      result = wrapper.transform(x, y)
      self.assertEqual(
          len(result.columns),
          4,
          "We expect 2 untouched features + f2 target encoded into 2 features",
      )
  
      # directly fit_transform()
      wrapper = PolynomialWrapper(
          encoders.LeaveOneOutEncoder(cols=["f2"])
      )
      result2 = wrapper.fit_transform(x, y)
      self.assertEqual(
          len(result2.columns),
          4,
          "We expect 2 untouched features + f2 target encoded into 2 features",
      )
  
      pd.testing.assert_frame_equal(result, result2)
  
file_path: category_encoders/tests/test_wrapper.py
imports: import numpy
  import pandas
  from unittest import TestCase
  from sklearn.model_selection import GroupKFold
  import category_encoders
  import tests.helpers
  from category_encoders.wrapper import PolynomialWrapper, NestedCVWrapper
label_desc: Use image node_id 2 for calling the TestMultiClassWrapper obj's underlying member method code with example usage: obj.test_transform_only_selected() without return types