text: import os
  from os import listdir
  from os.path import isfile, join
  import glob
  import argparse
  
  from rasa.cli.test import run_nlu_test
  from rasa.test import perform_nlu_cross_validation
  from rasa.shared.data import get_nlu_directory
  
  args = argparse.Namespace
  # onlyfiles = [f for f in listdir("pipelines/") if isfile(join("pipelines/", f))]
  # print (onlyfiles)
  pipelines = glob.glob("pipelines\\*")
  print(pipelines)
  nlu_data = get_nlu_directory("data")
  additional_args = {
      "loglevel": None,
      "model": "models",
      "stories": "tests",
      "max_stories": None,
      "endpoints": None,
      "fail_on_prediction_errors": False,
      "url": None,
      "evaluate_model_directory": False,
      "nlu": "data",
      "config": None,
      "cross_validation": True,
      "folds": 5,
      "runs": 3,
      "percentages": [0, 25, 50, 75],
      "disable_plotting": False,
      "successes": False,
      "no_errors": False,
      "out": "results",
      "errors": True,
  }
  
  
  for i in range(len(pipelines)):
      try:
          os.system(
              "rasa train nlu --config {} --out stack_models\\{}".format(
                  pipelines[i], pipelines[i]
              )
          )
          perform_nlu_cross_validation(
              "{}".format(pipelines[i]), nlu_data, "results\\{}".format(pipelines[i]), {}
          )
      except OSError:
          print("{} cannot find the model".format(pipelines[i]))
          continue
  