code: def test_limit_vocabulary_should_raise(self):
      # Given
      vectorizer = TfidfVectorizer()
      dataset = {
          "language": "en",
          "entities": dict(),
          "intents": dict(),
      }
      utterances = [text_to_utterance("5 55 6 66 666")]
  
      vectorizer.fit(utterances, dataset)
  
      # When / Then
      kept_indexes = ["7", "8"]
      with self.assertRaises(ValueError):
          vectorizer.limit_vocabulary(kept_indexes)
  
imports: from __future__ import unicode_literals
  import io
  from builtins import str, zip, range
  import numpy
  from checksumdir import dirhash
  from mock import patch, MagicMock
  from snips_nlu.common.io_utils import temp_dir
  from snips_nlu.common.utils import json_string
  from snips_nlu.constants import LANGUAGE_EN, STEMS, WORD_CLUSTERS, STOP_WORDS
  from snips_nlu.dataset import Dataset
  from snips_nlu.entity_parser import BuiltinEntityParser, CustomEntityParser
  from snips_nlu.entity_parser.custom_entity_parser_usage import CustomEntityParserUsage
  from snips_nlu.exceptions import _EmptyDatasetUtterancesError
  from snips_nlu.intent_classifier.featurizer import CooccurrenceVectorizer, Featurizer, TfidfVectorizer
  from snips_nlu.intent_classifier.log_reg_classifier_utils import text_to_utterance
  from snips_nlu.pipeline.configs import FeaturizerConfig
  from snips_nlu.pipeline.configs.intent_classifier import CooccurrenceVectorizerConfig, TfidfVectorizerConfig
  from snips_nlu.tests.utils import FixtureTest, get_empty_dataset, EntityParserMock
label_desc: Use image node_id 3 for calling the TestTfidfVectorizer obj's underlying member method code with example usage: obj.test_limit_vocabulary_should_raise() without return types