code: def train(
      dataset_path,
      output_path,
      config_path=None,
      verbose=False,
      random_seed=None,
  ):
      """Train an NLU engine on the provided dataset"""
      import json
      import logging
      from pathlib import Path
  
      from snips_nlu import SnipsNLUEngine
      from snips_nlu.cli.utils import set_nlu_logger
      from snips_nlu.common.utils import check_random_state
  
      if verbose == 1:
          set_nlu_logger(logging.INFO)
      elif verbose >= 2:
          set_nlu_logger(logging.DEBUG)
  
      with Path(dataset_path).open("r", encoding="utf8") as f:
          dataset = json.load(f)
  
      config = None
      if config_path is not None:
          with Path(config_path).open("r", encoding="utf8") as f:
              config = json.load(f)
  
      random_state = check_random_state(random_seed)
  
      print("Create and train the engine...")
      engine = SnipsNLUEngine(config, random_state=random_state).fit(
          dataset
      )
  
      print("Persisting the engine...")
      engine.persist(output_path)
  
      print("Saved the trained engine to %s" % output_path)
  
label_desc: Use image node_id 3 for calling a global function with example usage: train(dataset_path, output_path, config_path, verbose, random_seed) without return types