code: def assertClassifierPredictsProbabilities(
      self, classifier, sparsity_indicator="sparse"
  ):
      for X, y in self.get_multilabel_data_for_tests(
          sparsity_indicator
      ):
          (
              X_train,
              X_test,
              y_train,
              y_test,
          ) = model_selection.train_test_split(
              X, y, test_size=0.33, random_state=42
          )
          classifier.fit(X_train, y_train)
          result = classifier.predict_proba(X_test)
          result = result.todense()
  
          max_value = result.max()
          min_value = result.min()
  
          self.assertEqual(result.shape, (X_test.shape[0], y.shape[1]))
          self.assertGreaterEqual(np.round(max_value), 0.0)
          self.assertGreaterEqual(np.round(min_value), 0.0)
          self.assertLessEqual(np.round(min_value), 1.0)
          self.assertLessEqual(np.round(max_value), 1.0)
  
file_path: scikit-multilearn/skmultilearn/tests/classifier_basetest.py
imports: import unittest
  from sklearn import model_selection
  import numpy
  import scipy.sparse
  from .example import EXAMPLE_X, EXAMPLE_y
label_desc: Use image node_id 4 for calling the ClassifierBaseTest obj's underlying member method code with example usage: obj.assertClassifierPredictsProbabilities(classifier, sparsity_indicator) without return types