code: def test_normalize_false(self, normalize_spy, num_samples=100, n=10):
      dataset = random_sklearn_dataset(num_samples, vocab_size=10)
      strategy = SEALS(LeastConfidence(), k=5, normalize=False)
  
      indices_labeled = np.random.choice(
          np.arange(num_samples), size=10, replace=False
      )
      indices_unlabeled = np.array(
          [
              i
              for i in range(len(dataset))
              if i not in set(indices_labeled)
          ]
      )
  
      clf_mock = self._get_clf()
      if clf_mock is not None:
          proba = np.random.random_sample((num_samples, 2))
          clf_mock.predict_proba = Mock(return_value=proba)
  
      y = np.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1])
  
      strategy.query(
          clf_mock, dataset, indices_unlabeled, indices_labeled, y, n=n
      )
  
      normalize_spy.assert_not_called()
  
imports: import unittest
  import pytest
  import numpy
  from unittest.mock import Mock, patch
  from numpy.testing import assert_array_equal
  from sklearn.base import clone
  from sklearn.preprocessing import normalize
  from small_text.query_strategies.exceptions import EmptyPoolException
  from small_text.classifiers import ConfidenceEnhancedLinearSVC, SklearnClassifierFactory
  from small_text.query_strategies import BreakingTies, CategoryVectorInconsistencyAndRanking, ContrastiveActiveLearning, DiscriminativeActiveLearning, EmbeddingKMeans, GreedyCoreset, LeastConfidence, LightweightCoreset, SEALS, SubsamplingQueryStrategy
  from tests.unit.small_text.query_strategies.test_strategies import SamplingStrategiesTests, SklearnClassifierWithRandomEmbeddingsAndProba
  from tests.utils.datasets import random_sklearn_dataset, random_matrix_data
  from tests.utils.object_factory import get_initialized_active_learner
label_desc: Use image node_id 4 for calling the SEALSTest obj's underlying member method code with example usage: obj.test_normalize_false(normalize_spy, num_samples, n) without return types