class_docstr: Main class to use for intent parsing
  
  A :class:`SnipsNLUEngine` relies on a list of :class:`.IntentParser`
  object to parse intents, by calling them successively using the first
  positive output.
  
  With the default parameters, it will use the two following intent parsers
  in this order:
  
  - a :class:`.DeterministicIntentParser`
  - a :class:`.ProbabilisticIntentParser`
  
  The logic behind is to first use a conservative parser which has a very
  good precision while its recall is modest, so simple patterns will be
  caught, and then fallback on a second parser which is machine-learning
  based and will be able to parse unseen utterances while ensuring a good
  precision and recall.
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docstr: Whether or not the nlu engine has already been fitted
docstr_tok: ["Whether","or","not","the","nlu","engine","has","already","been","fitted"]
code: def fitted(self):
      """Whether or not the nlu engine has already been fitted"""
      return self.dataset_metadata is not None
  
imports: from __future__ import unicode_literals
  import json
  import logging
  from builtins import str
  from collections import defaultdict
  from pathlib import Path
  from future.utils import iteritems, itervalues
  from snips_nlu.__about__ import __model_version__, __version__
  from snips_nlu.common.dataset_utils import get_slot_name_mappings
  from snips_nlu.common.log_utils import log_elapsed_time
  from snips_nlu.common.utils import check_persisted_path, fitted_required, json_string
  from snips_nlu.constants import AUTOMATICALLY_EXTENSIBLE, BUILTIN_ENTITY_PARSER, CUSTOM_ENTITY_PARSER, ENTITIES, ENTITY_KIND, LANGUAGE, RESOLVED_VALUE, RES_ENTITY, RES_INTENT, RES_INTENT_NAME, RES_MATCH_RANGE, RES_PROBA, RES_SLOTS, RES_VALUE, RESOURCES, BYPASS_VERSION_CHECK
  from snips_nlu.dataset import validate_and_format_dataset
  from snips_nlu.default_configs import DEFAULT_CONFIGS
  from snips_nlu.entity_parser import CustomEntityParser
  from snips_nlu.entity_parser.builtin_entity_parser import BuiltinEntityParser, is_builtin_entity
  from snips_nlu.exceptions import InvalidInputError, IntentNotFoundError, LoadingError, IncompatibleModelError
  from snips_nlu.intent_parser import IntentParser
  from snips_nlu.pipeline.configs import NLUEngineConfig
  from snips_nlu.pipeline.processing_unit import ProcessingUnit
  from snips_nlu.resources import load_resources_from_dir, persist_resources
  from snips_nlu.result import builtin_slot, custom_slot, empty_result, extraction_result, is_empty, parsing_result
label_desc: Use image node_id 3 for calling the SnipsNLUEngine obj's underlying member method code with example usage: obj.fitted() and returns: unknown