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 _do_train_and_valid(self):
      if not self.do_train and self.do_valid:
          raise ValueError(
              "Cannot execute validation without training."
          )
  
      if self.do_train:
          train_data = self.train_data
          valid_data = self.valid_data if self.do_valid else None
  
          self.valid_result = self.trainer.fit(train_data, valid_data)
  
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 6 for calling the Experiment obj's underlying member method code with example usage: obj._do_train_and_valid() without return types