code: def convert_training_data(
      data_file: Union[list, Text],
      out_file: Text,
      output_format: Text,
      language: Text,
  ) -> None:
      """Convert training data.
  
      Args:
          data_file (Union[list, Text]): Path to the file or directory
              containing Rasa data.
          out_file (Text): File or existing path where to save
              training data in Rasa format.
          output_format (Text): Output format the training data
              should be converted into.
          language (Text): Language of the data.
      """
      if isinstance(data_file, list):
          data_file = data_file[0]
  
      if not os.path.exists(str(data_file)):
          print_error(
              "Data file '{}' does not exist. Provide a valid NLU data file using "
              "the '--data' argument.".format(data_file)
          )
          return
  
      td = rasa.shared.nlu.training_data.loading.load_data(
          data_file, language
      )
      if output_format == "json":
          output = td.nlu_as_json(indent=2)
      else:
          output = RasaYAMLWriter().dumps(td)
  
      write_to_file(out_file, output)
  
imports: import os
  from typing import Text, Union
  from rasa.shared.nlu.training_data.formats.rasa_yaml import RasaYAMLWriter
  from rasa.shared.utils.cli import print_error
  import rasa.shared.nlu.training_data.loading
  from rasa.nlu.utils import write_to_file
label_desc: Use image node_id 1 for calling a global function with example usage: convert_training_data(data_file, out_file, output_format, language) without return types