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: Whether or not the intent parser has already been fitted
docstr_tok: ["Whether","or","not","the","intent","parser","has","already","been","fitted"]
code: def fitted(self):
      """Whether or not the intent parser has already been fitted"""
      return (
          self.intent_classifier is not None
          and self.intent_classifier.fitted
          and all(
              slot_filler is not None and slot_filler.fitted
              for slot_filler in itervalues(self.slot_fillers)
          )
      )
  
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 2 for calling the ProbabilisticIntentParser obj's underlying member method code with example usage: obj.fitted() and returns: unknown