code: def test_default_configuration_negative_values(self):
      # Custon preprocessing test to check if clipping to zero works
      X_train, Y_train, X_test, Y_test = get_dataset(dataset="digits")
      ss = sklearn.preprocessing.StandardScaler()
      X_train = ss.fit_transform(X_train)
      configuration_space = (
          MultinomialNB.get_hyperparameter_search_space()
      )
      default = configuration_space.get_default_configuration()
  
      cls = MultinomialNB(
          random_state=1,
          **{
              hp_name: default[hp_name]
              for hp_name in default
              if default[hp_name] is not None
          },
      )
  
      cls = cls.fit(X_train, Y_train)
      prediction = cls.predict(X_test)
      self.assertAlmostEqual(
          np.nanmean(prediction == Y_test), 0.88888888888888884
      )
  
file_path: auto-sklearn/test/test_pipeline/components/classification/test_multinomial_nb.py
imports: import numpy
  import sklearn.naive_bayes
  import sklearn.preprocessing
  from autosklearn.pipeline.components.classification.multinomial_nb import MultinomialNB
  from autosklearn.pipeline.util import get_dataset
  from .test_base import BaseClassificationComponentTest
label_desc: Use image node_id 1 for calling the MultinomialNBComponentTest obj's underlying member method code with example usage: obj.test_default_configuration_negative_values() without return types