code: def test_invariance_to_data_types(self):
      x = np.array(
          [
              ["a", "b", "c"],
              ["a", "b", "c"],
              ["b", "b", "c"],
              ["b", "b", "b"],
              ["b", "b", "b"],
              ["a", "b", "a"],
          ]
      )
      y = [1, 2, 3, 3, 3, 3]
      wrapper = PolynomialWrapper(encoders.TargetEncoder())
      result = wrapper.fit_transform(x, y)
      th.verify_numeric(result)
  
      x = pd.DataFrame(
          [
              ["a", "b", "c"],
              ["a", "b", "c"],
              ["b", "b", "c"],
              ["b", "b", "b"],
              ["b", "b", "b"],
              ["a", "b", "a"],
          ],
          columns=["f1", "f2", "f3"],
      )
      y = ["bee", "cat", "dog", "dog", "dog", "dog"]
      wrapper = PolynomialWrapper(encoders.TargetEncoder())
      result2 = wrapper.fit_transform(x, y)
  
      self.assertTrue(
          (result.to_numpy() == result2.to_numpy()).all(),
          "The content should be the same regardless whether we pass Numpy or Pandas data type.",
      )
  
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 1 for calling the TestMultiClassWrapper obj's underlying member method code with example usage: obj.test_invariance_to_data_types() without return types