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: generate a numeric id for an intent
  
  Args:
      intent_name (str): intent name
  
  Returns:
      int: numeric id
docstr_tok: ["generate","a","numeric","id","for","an","intent","Args",":","intent_name","(","str",")",":","intent","name","Returns",":","int",":","numeric","id"]
code: def _get_intent_id(self, intent_name):
      """generate a numeric id for an intent
  
      Args:
          intent_name (str): intent name
  
      Returns:
          int: numeric id
  
      """
      intent_id = self._intents_mapping.get(intent_name)
      if intent_id is None:
          intent_id = len(self._intents_names)
          self._intents_names.append(intent_name)
          self._intents_mapping[intent_name] = intent_id
  
      return intent_id
  
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 13 for calling the LookupIntentParser obj's underlying member method code with example usage: obj._get_intent_id(intent_name) and returns: intent_id