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: The probabilistic intent parser can be configured by passing a
  :class:`.ProbabilisticIntentParserConfig`
docstr_tok: ["The","probabilistic","intent","parser","can","be","configured","by","passing","a",":","class",":","`",".ProbabilisticIntentParserConfig","`"]
code: def __init__(self, config=None, **shared):
      """The probabilistic intent parser can be configured by passing a
      :class:`.ProbabilisticIntentParserConfig`"""
      super(ProbabilisticIntentParser, self).__init__(config, **shared)
      self.intent_classifier = None
      self.slot_fillers = dict()
  
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 1 to create a new ProbabilisticIntentParser object from inherited base classes: IntentParser with example: obj = ProbabilisticIntentParser(config)