class_docstr: A fast running api, which includes the complete process of
  training and testing a model on a specified dataset
  
  Args:
      model (str): model name
      dataset (str): dataset name
      config_file_list (list): config files used to modify experiment parameters
      config_dict (dict): parameters dictionary used to modify experiment parameters
code: def _init_data(
      config: Config, accelerator: Accelerator
  ) -> Tuple[DataLoader, DataLoader, DataLoader, Any]:
      tokenizer = get_tokenizer(config)
      train_data, valid_data, test_data = data_preparation(
          config, tokenizer
      )
      train_data, valid_data, test_data = accelerator.prepare(
          train_data, valid_data, test_data
      )
      return train_data, valid_data, test_data, tokenizer
  
imports: import os
  import logging
  from copy import copy
  from accelerate.logging import get_logger
  from typing import Optional, Tuple, Any, List, Dict
  from accelerate import Accelerator
  from torch.utils.data import DataLoader
  from ..config.configurator import Config
  from ..data.utils import data_preparation
  from ..trainer.trainer import Trainer
  from ..utils.dashboard import SummaryTracker
  from ..utils.logger import init_logger
  from ..utils.utils import get_model, get_tokenizer, get_trainer, init_seed
label_desc: Use image node_id 4 for calling the Experiment obj's underlying member method code with example usage: obj._init_data(config, accelerator) and returns: train_data,  valid_data,  test_data,  tokenizer