class_docstr: Holds loaded intent and entity training data.
class_docstr_tok: ["Holds","loaded","intent","and","entity","training","data","."]
docstr: Generates yaml representation of the response phrases (NLG) of TrainingData.
  
  Returns:
      responses in yaml format as a string
docstr_tok: ["Generates","yaml","representation","of","the","response","phrases","(","NLG",")","of","TrainingData",".","Returns",":","responses","in","yaml","format","as","a","string"]
code: def nlg_as_yaml(self) -> Text:
      """Generates yaml representation of the response phrases (NLG) of TrainingData.
  
      Returns:
          responses in yaml format as a string
      """
      from rasa.shared.nlu.training_data.formats.rasa_yaml import (
          RasaYAMLWriter,
      )
  
      # only dump responses. at some point it might make sense to remove the
      # differentiation between dumping NLU and dumping responses. but we
      # can't do that until after we remove markdown support.
      return RasaYAMLWriter().dumps(
          TrainingData(responses=self.responses)
      )
  
imports: import logging
  import os
  from pathlib import Path
  import random
  from collections import Counter, OrderedDict
  import copy
  from os.path import relpath
  from typing import Any, Dict, List, Optional, Set, Text, Tuple, Callable
  import operator
  import rasa.shared.data
  from rasa.shared.utils.common import lazy_property
  import rasa.shared.utils.io
  from rasa.shared.nlu.constants import RESPONSE, INTENT_RESPONSE_KEY, ENTITY_ATTRIBUTE_TYPE, ENTITY_ATTRIBUTE_GROUP, ENTITY_ATTRIBUTE_ROLE, NO_ENTITY_TAG, INTENT, ENTITIES, TEXT, ACTION_NAME
  from rasa.shared.nlu.training_data.message import Message
  from rasa.shared.nlu.training_data import util
label_desc: Use image node_id 26 for calling the TrainingData obj's underlying member method code with example usage: obj.nlg_as_yaml() and returns: RasaYAMLWriter