code: def train(self, output_dir: str):
      from transformers import Trainer, TrainingArguments
  
      # check required path argument
      self.trainer_kwargs["output_dir"] = output_dir
  
      # prepare data
      self.init_model(new=True)
      self.preprocess_datasets()
  
      # get metrics function
      compute_metrics = self.compute_metrics()
  
      self.trainer_model.to(self.device)
      # set trainer
      if self.device == "cuda":
          self.trainer_kwargs["no_cuda"] = False
      elif self.device == "mps":
          self.trainer_kwargs["use_mps_device"] = True
      self._trainer = Trainer(
          args=TrainingArguments(**self.trainer_kwargs),
          model=self.trainer_model,
          tokenizer=self.trainer_tokenizer,
          train_dataset=self._tokenized_train_dataset,
          eval_dataset=self._tokenized_eval_dataset,
          compute_metrics=compute_metrics,
          data_collator=self._data_collator,
      )
  
      #  train
      self._trainer.train()
      if self._tokenized_eval_dataset:
          self._metrics = self._trainer.evaluate()
          self._logger.info(self._metrics)
      else:
          self._metrics = None
  
      self.save(output_dir)
  
      self.init_pipeline()
  
file_path: argilla/src/argilla/training/transformers.py
imports: import logging
  from typing import List, Union
  from datasets import DatasetDict
  from argilla.client.models import TextClassificationRecord, TokenClassificationRecord
  from argilla.training.base import ArgillaTrainerSkeleton
  from argilla.training.utils import _apply_column_mapping, filter_allowed_args, get_default_args
  from argilla.utils.dependency import require_dependencies
label_desc: Use image node_id 9 for calling the ArgillaTransformersTrainer obj's underlying member method code with example usage: obj.train(output_dir) without return types