code: def test_unique_column_is_not_predictive(self):
      # @ToDo not sure how useful this test is. TargetEncoders set the value to the default if there is only
      #  one category but they probably should not. See discussion in issue 327
      test_encoders = [
          "LeaveOneOutEncoder",
          "WOEEncoder",
          "MEstimateEncoder",
          "JamesSteinEncoder",
          "CatBoostEncoder",
          "GLMMEncoder",
      ]
      for encoder_name in test_encoders:
          enc = getattr(encoders, encoder_name)()
          with self.subTest(encoder_name=encoder_name):
              result = enc.fit_transform(X[["unique_str"]], y)
              self.assertTrue(
                  all(result.var() < 0.001),
                  "The unique string column must not be predictive of the label",
              )
  
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 24 for calling the TestEncoders obj's underlying member method code with example usage: obj.test_unique_column_is_not_predictive() without return types