code: def train_nlu(
      config: Text,
      nlu_data: Optional[Text],
      output: Text,
      fixed_model_name: Optional[Text] = None,
      persist_nlu_training_data: bool = False,
      additional_arguments: Optional[Dict] = None,
      domain: Optional[Union[Domain, Text]] = None,
      model_to_finetune: Optional[Text] = None,
      finetuning_epoch_fraction: float = 1.0,
  ) -> Optional[Text]:
      """Trains an NLU model.
  
      Args:
          config: Path to the config file for NLU.
          nlu_data: Path to the NLU training data.
          output: Output path.
          fixed_model_name: Name of the model to be stored.
          persist_nlu_training_data: `True` if the NLU training data should be persisted
                                     with the model.
          additional_arguments: Additional training parameters which will be passed to
                                the `train` method of each component.
          domain: Path to the optional domain file/Domain object.
          model_to_finetune: Optional path to a model which should be finetuned or
              a directory in case the latest trained model should be used.
          finetuning_epoch_fraction: The fraction currently specified training epochs
              in the model configuration which should be used for finetuning.
  
      Returns:
          Path to the model archive.
      """
      if not nlu_data:
          rasa.shared.utils.cli.print_error(
              "No NLU data given. Please provide NLU data in order to train "
              "a Rasa NLU model using the '--nlu' argument."
          )
          return None
  
      # training NLU only hence the training files still have to be selected
      file_importer = (
          TrainingDataImporter.load_nlu_importer_from_config(
              config,
              domain,
              training_data_paths=[nlu_data],
              args=additional_arguments,
          )
      )
  
      training_data = file_importer.get_nlu_data()
      if training_data.contains_no_pure_nlu_data():
          rasa.shared.utils.cli.print_error(
              f"Path '{nlu_data}' doesn't contain valid NLU data in it. "
              f"Please verify the data format. "
              f"The NLU model training will be skipped now."
          )
          return None
  
      return _train_graph(
          file_importer,
          training_type=TrainingType.NLU,
          output_path=output,
          model_to_finetune=model_to_finetune,
          fixed_model_name=fixed_model_name,
          finetuning_epoch_fraction=finetuning_epoch_fraction,
          persist_nlu_training_data=persist_nlu_training_data,
          **(additional_arguments or {}),
      ).model
  
imports: import time
  from pathlib import Path
  from typing import Text, NamedTuple, Optional, List, Union, Dict, Any
  import randomname
  import rasa.engine.validation
  from rasa.engine.caching import LocalTrainingCache
  from rasa.engine.recipes.recipe import Recipe
  from rasa.engine.runner.dask import DaskGraphRunner
  from rasa.engine.storage.local_model_storage import LocalModelStorage
  from rasa.engine.storage.storage import ModelStorage
  from rasa.engine.training.components import FingerprintStatus
  from rasa.engine.training.graph_trainer import GraphTrainer
  from rasa.shared.core.events import SlotSet
  from rasa.shared.core.training_data.structures import StoryGraph
  from rasa.shared.data import TrainingType
  from rasa.shared.importers.importer import TrainingDataImporter
  from rasa import telemetry
  from rasa.shared.core.domain import Domain
  import rasa.utils.common
  import rasa.shared.utils.common
  import rasa.shared.utils.cli
  import rasa.shared.exceptions
  import rasa.shared.utils.io
  import rasa.shared.constants
  import rasa.model
label_desc: Use image node_id 9 for calling a global function with example usage: train_nlu(config, nlu_data, output, fixed_model_name, persist_nlu_training_data, additional_arguments, domain, model_to_finetune, finetuning_epoch_fraction) and returns: _train_graph, None, None