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 _on_experiment_end(self):
      if self.config["max_save"] == 0:
          saved_dir = os.path.abspath(
              os.path.join(
                  self.config["saved_dir"],
                  self.config["filename"],
                  "checkpoint_best",
              )
          )
          saved_link = (
              os.readlink(saved_dir)
              if os.path.exists(saved_dir)
              else ""
          )
          from ..utils import safe_remove
  
          safe_remove(saved_dir)
          safe_remove(saved_link)
      self.__extended_config = None
  
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 8 for calling the Experiment obj's underlying member method code with example usage: obj._on_experiment_end() without return types