code: def remove_untrainable_entities_from(
      example: Dict[Text, Any]
  ) -> None:
      """Remove untrainable entities from serialised training example `example`.
  
      Entities with an untrainable extractor will be removed. Untrainable extractors
      are defined in `rasa.nlu.constants.PRETRAINED_EXTRACTORS`.
  
      Args:
          example: Serialised training example to inspect.
      """
      example_entities = example.get(ENTITIES)
  
      if not example_entities:
          # example contains no entities, so there's nothing to do
          return None
  
      trainable_entities = []
  
      for entity in example_entities:
          if entity.get(EXTRACTOR) in PRETRAINED_EXTRACTORS:
              logger.debug(
                  f"Excluding entity '{json.dumps(entity)}' from training data. "
                  f"Entity examples extracted by the following classes are not "
                  f"dumped to training data in markdown format: "
                  f"`{'`, `'.join(sorted(PRETRAINED_EXTRACTORS))}`."
              )
          else:
              trainable_entities.append(entity)
  
      example[ENTITIES] = trainable_entities
  
imports: import json
  import logging
  import os
  import re
  from typing import Any, Dict, Optional, Text, Match, List
  import scipy.sparse
  from rasa.shared.nlu.constants import ENTITIES, EXTRACTOR, PRETRAINED_EXTRACTORS, ENTITY_ATTRIBUTE_START, ENTITY_ATTRIBUTE_END, ENTITY_ATTRIBUTE_VALUE, ENTITY_ATTRIBUTE_TYPE, ENTITY_ATTRIBUTE_ROLE, ENTITY_ATTRIBUTE_GROUP
  from rasa.shared.constants import UTTER_PREFIX
  import rasa.shared.utils.io
  import rasa.shared.data
label_desc: Use image node_id 4 for calling a global function with example usage: remove_untrainable_entities_from(example) and returns: None