code: def debug_training(dataset_path, config_path=None):
      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 = NLUEngineConfig.from_dict(json.load(f))
  
      engine = SnipsNLUEngine(config).fit(dataset)
  
      while True:
          query = input("Enter a query (type 'q' to quit): ").strip()
          if isinstance(query, bytes):
              query = query.decode("utf8")
          if query == "q":
              break
          print(json.dumps(engine.parse(query), indent=2))
  
file_path: snips-nlu/debug/debug.py
imports: from __future__ import unicode_literals, print_function
  import argparse
  import json
  from builtins import input, bytes
  from pathlib import Path
  from snips_nlu import SnipsNLUEngine
  from snips_nlu.pipeline.configs.nlu_engine import NLUEngineConfig
label_desc: Use image node_id 1 for calling a global function with example usage: debug_training(dataset_path, config_path) without return types