code: def _get_entity_placeholders(dataset, language):
      return {
          e: _get_entity_name_placeholder(e, language)
          for e in dataset[ENTITIES]
      }
  
imports: from __future__ import unicode_literals
  import json
  import logging
  import re
  from builtins import str
  from collections import defaultdict
  from pathlib import Path
  from future.utils import iteritems, itervalues
  from snips_nlu.common.dataset_utils import get_slot_name_mappings
  from snips_nlu.common.log_utils import log_elapsed_time, log_result
  from snips_nlu.common.utils import check_persisted_path, deduplicate_overlapping_items, fitted_required, json_string, ranges_overlap, regex_escape, replace_entities_with_placeholders
  from snips_nlu.constants import DATA, END, ENTITIES, ENTITY, INTENTS, LANGUAGE, RES_INTENT, RES_INTENT_NAME, RES_MATCH_RANGE, RES_SLOTS, RES_VALUE, SLOT_NAME, START, TEXT, UTTERANCES, RES_PROBA
  from snips_nlu.dataset import validate_and_format_dataset
  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 DeterministicIntentParserConfig
  from snips_nlu.preprocessing import normalize_token, tokenize, tokenize_light
  from snips_nlu.resources import get_stop_words
  from snips_nlu.result import empty_result, extraction_result, intent_classification_result, parsing_result, unresolved_slot
label_desc: Use image node_id 3 for calling a global function with example usage: _get_entity_placeholders(dataset, language) and returns: unknown