code: def test_should_retrain_intent_classifier_when_force_retrain(self):
      # Given
      dataset_stream = io.StringIO(
          """
  ---
  type: intent
  name: MakeTea
  utterances:
  - make me a [beverage_temperature:Temperature](hot) cup of tea
  - make me [number_of_cups:snips/number](five) tea cups
  
  ---
  type: intent
  name: MakeCoffee
  utterances:
  - make me [number_of_cups:snips/number](one) cup of coffee please
  - brew [number_of_cups] cups of coffee"""
      )
      dataset = Dataset.from_yaml_files("en", [dataset_stream]).json
      parser = ProbabilisticIntentParser()
      intent_classifier = LogRegIntentClassifier()
      intent_classifier.fit(dataset)
      parser.intent_classifier = intent_classifier
  
      # When / Then
      with patch(
          "snips_nlu.intent_classifier.log_reg_classifier"
          ".LogRegIntentClassifier.fit"
      ) as mock_fit:
          parser.fit(dataset, force_retrain=True)
          mock_fit.assert_called_once()
  
imports: from __future__ import unicode_literals
  import io
  from future.utils import itervalues
  from mock import patch
  from snips_nlu.constants import RES_ENTITY, RES_INTENT, RES_INTENT_NAME, RES_SLOTS, RES_VALUE, RANDOM_STATE
  from snips_nlu.dataset import Dataset
  from snips_nlu.exceptions import IntentNotFoundError, NotTrained
  from snips_nlu.intent_classifier import IntentClassifier, LogRegIntentClassifier
  from snips_nlu.intent_parser import ProbabilisticIntentParser
  from snips_nlu.pipeline.configs import CRFSlotFillerConfig, LogRegIntentClassifierConfig, ProbabilisticIntentParserConfig
  from snips_nlu.result import unresolved_slot, intent_classification_result
  from snips_nlu.slot_filler import SlotFiller
  from snips_nlu.tests.utils import FixtureTest, MockIntentClassifier, MockSlotFiller
label_desc: Use image node_id 8 for calling the TestProbabilisticIntentParser obj's underlying member method code with example usage: obj.test_should_retrain_intent_classifier_when_force_retrain() without return types