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_test(self):
      if self.do_test:
          with self.summary_tracker.new_epoch("eval"):
              self.test_result = self.trainer.evaluate(
                  self.test_data, load_best_model=self.do_train
              )
              self.summary_tracker.set_metrics_results(self.test_result)
              self.logger.info(
                  "Evaluation result:\n{}".format(
                      self.summary_tracker._current_epoch.as_str(
                          sep=",\n", indent=" "
                      )
                  )
              )
  
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 7 for calling the Experiment obj's underlying member method code with example usage: obj._do_test() without return types