docstr: Updates the configuration of the trainer, but the parameters depend on the trainer.subclass.
docstr_tok: ["Updates","the","configuration","of","the","trainer",",","but","the","parameters","depend","on","the","trainer.subclass","."]
code: def update_config(self, **kwargs) -> None:
      """
      Updates the configuration of the trainer, but the parameters depend on the trainer.subclass.
      """
      self.model_kwargs.update(
          filter_allowed_args(self._trainer_cls.__init__, **kwargs)
      )
      self.trainer_kwargs.update(
          filter_allowed_args(self._trainer_cls.fit, **kwargs)
      )
  
      if "batch_size" in kwargs:
          self.data_kwargs["batch_size"] = kwargs.pop("batch_size")
  
      # These parameters are allowed, but for simplicity
      # they are passed as self._model as positional argument to avoid errors
      # between Cross-Encoder/Bi-encoder.
      if "model_name_or_path" in kwargs:
          self._model = self.model_kwargs.pop("model_name_or_path")
      if "model_name" in kwargs:
          self._model = self.model_kwargs.pop("model_name")
  
      if "loss_cls" in kwargs:
          self._loss_cls = kwargs.pop("loss_cls")
  
      if "evaluator" in kwargs:
          self._set_evaluator(evaluator_cls=kwargs.pop("evaluator"))
  
file_path: argilla/src/argilla/client/feedback/training/frameworks/sentence_transformers.py
imports: import logging
  from typing import TYPE_CHECKING, List, Optional, Tuple, Union
  from argilla.client.feedback.training.base import ArgillaTrainerSkeleton
  from argilla.client.feedback.training.schemas.base import TrainingTaskForSentenceSimilarity
  from argilla.training.utils import filter_allowed_args, get_default_args
  from argilla.utils.dependency import require_dependencies
label_desc: Use image node_id 6 for calling the ArgillaSentenceTransformersTrainer obj's underlying member method code with example usage: obj.update_config() without return types