docstr: Train the pipeline using `spaCy`.
  
  Args:
      output_dir: A `str` with the path to save the trained pipeline. Defaults to None.
docstr_tok: ["Train","the","pipeline","using","`","spaCy","`",".","Args",":","output_dir",":","A","`","str","`","with","the","path","to","save","the","trained","pipeline",".","Defaults","to","None","."]
code: def train(self, output_dir: Optional[str] = None) -> None:
      """Train the pipeline using `spaCy`.
  
      Args:
          output_dir: A `str` with the path to save the trained pipeline. Defaults to None.
      """
      from spacy.training.initialize import init_nlp
      from spacy.training.loop import train as train_nlp
  
      self._logger.warning(
          "Note that the spaCy training is expected to be used through the CLI rather"
          " than programmatically, so the dataset needs to be dumped into the disk and"
          " then loaded from disk. More information at"
          " https://spacy.io/usage/training#api"
      )
      self._logger.info(
          f"Dumping the train dataset to {self._train_dataset_path}"
      )
      self._train_dataset.to_disk(self._train_dataset_path)
      if self._eval_dataset:
          self._logger.info(
              f"Dumping the dev dataset to {self._eval_dataset_path}"
          )
          self._eval_dataset.to_disk(self._eval_dataset_path)
  
      # Both `init_nlp` and `train_nlp` must be executed in the same Jupyter Notebook
      # cell if using the GPU, otherwise, since `thinc` is using `ContextVars` to
      # store the `Config` object, the `Config` object will be lost between cells and
      # the training will fail.
      self.trainer_model = init_nlp(
          self.trainer_kwargs, use_gpu=self.gpu_id
      )
      self.trainer_model, _ = train_nlp(
          self.trainer_model,
          use_gpu=self.gpu_id,
          stdout=sys.stdout,
          stderr=sys.stderr,
      )
  
      if output_dir:
          self.save(output_dir)
  
imports: import logging
  import sys
  from pathlib import Path
  from typing import Any, Dict, List, Literal, Optional, Union
  from pydantic import BaseModel
  from argilla.client.models import TextClassificationRecord, TokenClassificationRecord
  from argilla.training.base import ArgillaTrainerSkeleton
  from argilla.utils.dependency import require_dependencies
label_desc: Use image node_id 5 for calling the _ArgillaSpaCyTrainerBase obj's underlying member method code with example usage: obj.train(output_dir) without return types