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__(
      self,
      model: Optional[str] = None,
      dataset: Optional[str] = None,
      config_file_list: Optional[List[str]] = None,
      config_dict: Optional[Dict[str, Any]] = None,
  ):
      self.config = Config(
          model, dataset, config_file_list, config_dict
      )
      wandb_setting = "wandb " + self.config["wandb"]
      os.system(wandb_setting)
      self.__extended_config = None
  
      from accelerate import DistributedDataParallelKwargs
  
      ddp_kwargs = DistributedDataParallelKwargs(
          find_unused_parameters=self.config["find_unused_parameters"]
      )
      self.accelerator = Accelerator(
          gradient_accumulation_steps=self.config["accumulation_steps"],
          kwargs_handlers=[ddp_kwargs],
      )
      self.config.update(
          {
              "_is_local_main_process": self.accelerator.is_local_main_process,
              "device": self.accelerator.device,
          }
      )
      self.logger = self.init_logger(self.config)
      self.summary_tracker = SummaryTracker.basicConfig(
          self.get_config()
      )
      (
          self.train_data,
          self.valid_data,
          self.test_data,
          self.tokenizer,
      ) = self._init_data(self.get_config(), self.accelerator)
  
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 1 to create a new Experiment object  with example: obj = Experiment(model, dataset, config_file_list, config_dict)