code: def __init__(self, *args, **kwargs):
      # init ArgillaTrainerSkeleton
      ArgillaTrainerSkeleton.__init__(self, *args, **kwargs)
  
      import torch
      from transformers import (
          AutoModelForQuestionAnswering,
          AutoModelForSequenceClassification,
          set_seed,
      )
  
      model = kwargs.get("model", None)
      self.trainer_model = (
          model if model and not isinstance(model, str) else None
      )
      self.trainer_tokenizer = kwargs.get("tokenizer", None)
      self.trainer_pipeline = None
  
      self.device = "cpu"
      if torch.backends.mps.is_available():
          self.device = "mps"
      elif torch.cuda.is_available():
          self.device = "cuda"
  
      if self._seed is None:
          self._seed = 42
      set_seed(self._seed)
  
      if self._model is None:
          self._model = "bert-base-cased"
  
      if isinstance(self._dataset, DatasetDict):
          self._train_dataset = self._dataset["train"]
          self._eval_dataset = self._dataset["test"]
      elif isinstance(self._dataset, Dataset):
          self._train_dataset = self._dataset
          self._eval_dataset = None
      else:
          raise NotImplementedError(
              f"We do not support {type(self._dataset)} yet."
          )
  
      if isinstance(self._task, TrainingTaskForTextClassification):
          self._model_class = AutoModelForSequenceClassification
      elif isinstance(self._task, TrainingTaskForQuestionAnswering):
          self._model_class = AutoModelForQuestionAnswering
      else:
          raise NotImplementedError(
              f"ArgillaTransformersTrainer does not support {self._task.__class__.__name__} yet."
          )
  
      self.init_training_args()
  
file_path: argilla/src/argilla/client/feedback/training/frameworks/transformers.py
imports: from typing import TYPE_CHECKING
  from datasets import Dataset, DatasetDict
  from argilla.client.feedback.training.base import ArgillaTrainerSkeleton
  from argilla.client.feedback.training.schemas.base import TrainingTaskForQuestionAnswering, TrainingTaskForTextClassification
  from argilla.training.transformers import ArgillaTransformersTrainer
  from argilla.utils.dependency import requires_dependencies
label_desc: Use image node_id 1 to create a new ArgillaTransformersTrainer object from inherited base classes: ArgillaTransformersTrainerV1, ArgillaTrainerSkeleton with example: obj = ArgillaTransformersTrainer()