class_docstr: Intent parser which consists in two steps: intent classification then
  slot filling
class_docstr_tok: ["Intent","parser","which","consists","in","two","steps",":","intent","classification","then","slot","filling"]
docstr: Returns the list of intents ordered by decreasing probability
  
  The length of the returned list is exactly the number of intents in the
  dataset + 1 for the None intent
docstr_tok: ["Returns","the","list","of","intents","ordered","by","decreasing","probability","The","length","of","the","returned","list","is","exactly","the","number","of","intents","in","the","dataset","+","1","for","the","None","intent"]
code: def get_intents(self, text):
      """Returns the list of intents ordered by decreasing probability
  
      The length of the returned list is exactly the number of intents in the
      dataset + 1 for the None intent
      """
      return self.intent_classifier.get_intents(text)
  
imports: from __future__ import unicode_literals
  import json
  import logging
  from builtins import str
  from copy import deepcopy
  from datetime import datetime
  from pathlib import Path
  from future.utils import iteritems, itervalues
  from snips_nlu.common.log_utils import log_elapsed_time, log_result
  from snips_nlu.common.utils import check_persisted_path, elapsed_since, fitted_required, json_string
  from snips_nlu.constants import INTENTS, RES_INTENT_NAME
  from snips_nlu.dataset import validate_and_format_dataset
  from snips_nlu.exceptions import IntentNotFoundError, LoadingError
  from snips_nlu.intent_classifier import IntentClassifier
  from snips_nlu.intent_parser.intent_parser import IntentParser
  from snips_nlu.pipeline.configs import ProbabilisticIntentParserConfig
  from snips_nlu.result import parsing_result, extraction_result
  from snips_nlu.slot_filler import SlotFiller
label_desc: Use image node_id 5 for calling the ProbabilisticIntentParser obj's underlying member method code with example usage: obj.get_intents(text) and returns: self