code: def test_enrich_utterance(self):
      # Given
      utterances = [
          {
              "data": [
                  {"text": "one", "entity": "snips/number"},
                  {
                      "text": "beauty world",
                  },
                  {"text": "ent 1", "entity": "dummy_entity_1"},
              ]
          },
          text_to_utterance("one beauty world ent 1"),
          text_to_utterance("hello world entity_2"),
          text_to_utterance("bird bird"),
      ]
  
      builtin_ents = [
          [
              {
                  "value": "one",
                  "resolved_value": 1,
                  "range": {"start": 0, "end": 3},
                  "entity_kind": "snips/number",
              }
          ],
          [
              {
                  "value": "one",
                  "resolved_value": 1,
                  "range": {"start": 0, "end": 3},
                  "entity_kind": "snips/number",
              },
              {
                  "value": "1",
                  "resolved_value": 1,
                  "range": {"start": 27, "end": 28},
                  "entity_kind": "snips/number",
              },
          ],
          [
              {
                  "value": "2",
                  "resolved_value": 2,
                  "range": {"start": 19, "end": 20},
                  "entity_kind": "snips/number",
              }
          ],
          [],
      ]
  
      custom_ents = [
          [
              {
                  "value": "ent 1",
                  "resolved_value": "entity 1",
                  "range": {"start": 20, "end": 28},
                  "entity_kind": "dummy_entity_1",
              }
          ],
          [
              {
                  "value": "ent 1",
                  "resolved_value": "entity 1",
                  "range": {"start": 20, "end": 28},
                  "entity_kind": "dummy_entity_1",
              }
          ],
          [
              {
                  "value": "entity_2",
                  "resolved_value": "Éntity_2",
                  "range": {"start": 12, "end": 20},
                  "entity_kind": "dummy_entity_2",
              }
          ],
          [],
      ]
  
      w_clusters = [["111", "112"], ["111", "112"], [], []]
  
      vectorizer = TfidfVectorizer()
      vectorizer._language = "en"
  
      # When
      enriched_utterances = [
          vectorizer._enrich_utterance(*data)
          for data in zip(
              utterances, builtin_ents, custom_ents, w_clusters
          )
      ]
  
      # Then
      expected_u0 = (
          "beauty world ent 1 "
          "builtinentityfeaturesnipsnumber "
          "entityfeaturedummy_entity_1 111 112"
      )
  
      expected_u1 = (
          "one beauty world ent 1 "
          "builtinentityfeaturesnipsnumber "
          "builtinentityfeaturesnipsnumber "
          "entityfeaturedummy_entity_1 111 112"
      )
  
      expected_u2 = (
          "hello world entity_2 builtinentityfeaturesnipsnumber "
          "entityfeaturedummy_entity_2"
      )
  
      expected_u3 = "bird bird"
  
      expected_utterances = [
          expected_u0,
          expected_u1,
          expected_u2,
          expected_u3,
      ]
  
      self.assertEqual(expected_utterances, enriched_utterances)
  
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 1 for calling the TestTfidfVectorizer obj's underlying member method code with example usage: obj.test_enrich_utterance() without return types