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: Extracts slots from a text input, with the knowledge of the intent
  
  Args:
      text (str): input
      intent (str): the intent which the input corresponds to
  
  Returns:
      list: the list of extracted slots
  
  Raises:
      IntentNotFoundError: When the intent was not part of the training
          data
docstr_tok: ["Extracts","slots","from","a","text","input",",","with","the","knowledge","of","the","intent","Args",":","text","(","str",")",":","input","intent","(","str",")",":","the","intent","which","the","input","corresponds","to","Returns",":","list",":","the","list","of","extracted","slots","Raises",":","IntentNotFoundError",":","When","the","intent","was","not","part","of","the","training","data"]
code: def get_slots(self, text, intent):
      """Extracts slots from a text input, with the knowledge of the intent
  
      Args:
          text (str): input
          intent (str): the intent which the input corresponds to
  
      Returns:
          list: the list of extracted slots
  
      Raises:
          IntentNotFoundError: When the intent was not part of the training
              data
      """
      if intent is None:
          return []
  
      if intent not in self.slot_fillers:
          raise IntentNotFoundError(intent)
      return self.slot_fillers[intent].get_slots(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 6 for calling the ProbabilisticIntentParser obj's underlying member method code with example usage: obj.get_slots(text, intent) and returns: self, list