code: def test_classification(self):
      for encoder_name in encoders.__all__:
          with self.subTest(encoder_name=encoder_name):
              cols = [
                  "unique_str",
                  "underscore",
                  "extra",
                  "none",
                  "invariant",
                  "categorical",
                  "na_categorical",
                  "categorical_int",
              ]
  
              enc = getattr(encoders, encoder_name)(cols=cols)
              enc.fit(X, np_y)
              th.verify_numeric(enc.transform(X_t))
  
              enc = getattr(encoders, encoder_name)(verbose=1)
              enc.fit(X, np_y)
              th.verify_numeric(enc.transform(X_t))
  
              enc = getattr(encoders, encoder_name)(drop_invariant=True)
              enc.fit(X, np_y)
              th.verify_numeric(enc.transform(X_t))
  
              enc = getattr(encoders, encoder_name)(return_df=False)
              enc.fit(X, np_y)
              self.assertTrue(
                  isinstance(enc.transform(X_t), np.ndarray)
              )
              self.assertEqual(
                  enc.transform(X_t).shape[0],
                  X_t.shape[0],
                  "Row count must not change",
              )
  
              # encoders should be re-fittable (c.f. issue 122)
              X_a = pd.DataFrame(
                  data=["1", "2", "2", "2", "2", "2"], columns=["col_a"]
              )
              X_b = pd.DataFrame(
                  data=["1", "1", "1", "2", "2", "2"], columns=["col_b"]
              )  # different values and name
              y_dummy = [True, False, True, False, True, False]
              enc = getattr(encoders, encoder_name)()
              enc.fit(X_a, y_dummy)
              enc.fit(X_b, y_dummy)
              th.verify_numeric(enc.transform(X_b))
  
imports: import warnings
  from datetime import timedelta
  import numpy
  from numpy.testing import assert_array_equal
  import pandas
  import sklearn
  import tests.helpers
  from sklearn.utils.estimator_checks import check_transformer_general, check_transformers_unfitted, check_n_features_in
  from sklearn.compose import ColumnTransformer
  from unittest import TestCase
  from copy import deepcopy
  import category_encoders
label_desc: Use image node_id 2 for calling the TestEncoders obj's underlying member method code with example usage: obj.test_classification() without return types