text: from rasa_nlu.training_data import load_data
  from rasa_nlu.config import RasaNLUModelConfig
  from rasa_nlu.model import Trainer
  from rasa_nlu import config
  
  # loading the nlu training samples
  training_data = load_data("data/nlu.md")
  #training_data = load_data("data/nlu_data.json")
  
  # trainer to educate our pipeline
  trainer = Trainer(config.load("config.yml"))
  
  # train the model!
  interpreter = trainer.train(training_data)
  
  # store it for future use
  model_directory = trainer.persist("models/nlu", fixed_model_name="current")
  
  
  
  
  # A helper function for prettier output
  import json
  
  def pprint(o):   
      print(json.dumps(o, indent=2))
  
  #pprint(interpreter.parse("mumbai"))