code: def get_mocked_augment_utterances(
      dataset,
      intent_name,
      language,
      min_utterances,
      capitalization_ratio,
      add_builtin_entities_examples,
      resources,
      random_state,
  ):
      return dataset[INTENTS][intent_name][UTTERANCES]
  
imports: from __future__ import unicode_literals
  import io
  import sys
  from builtins import str
  from unittest import skipIf
  from checksumdir import dirhash
  from mock import patch
  from snips_nlu.common.io_utils import temp_dir
  from snips_nlu.constants import INTENTS, LANGUAGE_EN, RES_INTENT_NAME, RES_PROBA, UTTERANCES
  from snips_nlu.dataset import Dataset
  from snips_nlu.exceptions import NotTrained
  from snips_nlu.intent_classifier import LogRegIntentClassifier
  from snips_nlu.intent_classifier.featurizer import Featurizer
  from snips_nlu.intent_classifier.log_reg_classifier_utils import text_to_utterance
  from snips_nlu.pipeline.configs import LogRegIntentClassifierConfig
  from snips_nlu.result import intent_classification_result
  from snips_nlu.tests.utils import FixtureTest, get_empty_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: get_mocked_augment_utterances(dataset, intent_name, language, min_utterances, capitalization_ratio, add_builtin_entities_examples, resources, random_state) and returns: dataset