class_docstr: A deterministic Intent parser implementation based on a dictionary
  
  This intent parser is very strict by nature, and tends to have a very good
  precision but a low recall. For this reason, it is interesting to use it
  first before potentially falling back to another parser.
class_docstr_tok: ["A","deterministic","Intent","parser","implementation","based","on","a","dictionary","This","intent","parser","is","very","strict","by","nature",",","and","tends","to","have","a","very","good","precision","but","a","low","recall",".","For","this","reason",",","it","is","interesting","to","use","it","first","before","potentially","falling","back","to","another","parser","."]
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._intents_names:
          raise IntentNotFoundError(intent)
  
      slots = self.parse(text, intents=[intent])[RES_SLOTS]
      if slots is None:
          slots = []
      return slots
  
imports: from __future__ import unicode_literals
  import json
  import logging
  from builtins import str
  from collections import defaultdict
  from itertools import combinations
  from pathlib import Path
  from future.utils import iteritems, itervalues
  from snips_nlu_utils import normalize, hash_str
  from snips_nlu.common.log_utils import log_elapsed_time, log_result
  from snips_nlu.common.utils import check_persisted_path, deduplicate_overlapping_entities, fitted_required, json_string
  from snips_nlu.constants import DATA, END, ENTITIES, ENTITY, ENTITY_KIND, INTENTS, LANGUAGE, RES_INTENT, RES_INTENT_NAME, RES_MATCH_RANGE, RES_SLOTS, SLOT_NAME, START, TEXT, UTTERANCES, RES_PROBA
  from snips_nlu.dataset import validate_and_format_dataset, extract_intent_entities
  from snips_nlu.dataset.utils import get_stop_words_whitelist
  from snips_nlu.entity_parser.builtin_entity_parser import is_builtin_entity
  from snips_nlu.exceptions import IntentNotFoundError, LoadingError
  from snips_nlu.intent_parser.intent_parser import IntentParser
  from snips_nlu.pipeline.configs import LookupIntentParserConfig
  from snips_nlu.preprocessing import tokenize_light
  from snips_nlu.resources import get_stop_words
  from snips_nlu.result import empty_result, intent_classification_result, parsing_result, unresolved_slot, extraction_result
label_desc: Use image node_id 11 for calling the LookupIntentParser obj's underlying member method code with example usage: obj.get_slots(text, intent) and returns: slots, list