code: def train(
      name: str = typer.Option(
          default=None,
          help="The name of the dataset to be used for training.",
      ),
      framework: Framework = typer.Option(
          default=None, help="The framework to be used for training."
      ),
      workspace: str = typer.Option(
          default=None, help="The workspace to be used for training."
      ),
      limit: int = typer.Option(
          default=None, help="The number of record to be used."
      ),
      query: str = typer.Option(
          default=None, help="The query to be used."
      ),
      model: str = typer.Option(
          default=None,
          help="The modelname or path to be used for training.",
      ),
      train_size: float = typer.Option(
          default=1.0, help="The train split to be used."
      ),
      seed: int = typer.Option(
          default=42, help="The random seed number."
      ),
      device: int = typer.Option(
          default=-1, help="The GPU id to be used for training."
      ),
      output_dir: str = typer.Option(
          default="model", help="Output directory for the saved model."
      ),
      update_config_kwargs: str = typer.Option(
          default={},
          help="update_config() kwargs to be passed as a dictionary.",
      ),
  ):
      import json
  
      from argilla.cli.callback import init_callback
      from argilla.client.singleton import init
      from argilla.training import ArgillaTrainer
  
      init_callback()
  
      trainer = ArgillaTrainer(
          name=name,
          framework=framework,
          workspace=workspace,
          model=model,
          train_size=train_size,
          seed=seed,
          gpu_id=device,
          limit=limit,
          query=query,
      )
      trainer.update_config(**json.loads(update_config_kwargs))
      if output_dir:
          trainer.train(output_dir=output_dir)
      else:
          trainer.train()
  
imports: import typer
  from argilla.client.models import Framework
label_desc: Use image node_id 2 for calling a global function with example usage: train(name, framework, workspace, limit, query, model, train_size, seed, device, output_dir, update_config_kwargs) without return types