class_docstr: Dataset used in the main NLU training API
  
  Consists of intents and entities data. This object can be built either from
  text files (:meth:`.Dataset.from_files`) or from YAML files
  (:meth:`.Dataset.from_yaml_files`).
  
  Attributes:
      language (str): language of the intents
      intents (list of :class:`.Intent`): intents data
      entities (list of :class:`.Entity`): entities data
code: def _get_entity_values(self, entity):
      from snips_nlu_parsers import get_builtin_entity_examples
  
      if entity.is_builtin:
          return cycle(
              get_builtin_entity_examples(entity.name, self.language)
          )
      values = [
          v
          for utterance in entity.utterances
          for v in utterance.variations
      ]
      values_set = set(values)
      for intent in self.intents:
          for utterance in intent.utterances:
              for chunk in utterance.slot_chunks:
                  if not chunk.text or chunk.entity != entity.name:
                      continue
                  if chunk.text not in values_set:
                      values_set.add(chunk.text)
                      values.append(chunk.text)
      return cycle(values)
  
file_path: snips-nlu/snips_nlu/dataset/dataset.py
imports: from __future__ import print_function, unicode_literals
  import io
  from itertools import cycle
  from snips_nlu.common.utils import unicode_string
  from snips_nlu.dataset.entity import Entity
  from snips_nlu.dataset.intent import Intent
  from snips_nlu.exceptions import DatasetFormatError
label_desc: Use image node_id 6 for calling the Dataset obj's underlying member method code with example usage: obj._get_entity_values(entity) and returns: cycle, cycle