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
docstr: (Re-)initialize configuration. Since for now config and trainer is modifiable, this
  function is needed to ensure they were aligned to initial configuration.
docstr_tok: ["(","Re-",")","initialize","configuration",".","Since","for","now","config","and","trainer","is","modifiable",",","this","function","is","needed","to","ensure","they","were","aligned","to","initial","configuration","."]
code: def _on_experiment_start(self, extended_config: Optional[dict]):
      """(Re-)initialize configuration. Since for now config and trainer is modifiable, this
      function is needed to ensure they were aligned to initial configuration.
      """
      self.__extended_config = extended_config
      config = self.get_config()
      init_seed(config["seed"], config["reproducibility"])
  
      self.model = get_model(config["model"])(
          config, self.tokenizer
      ).to(config["device"])
      self.logger.info(self.model)
      self.trainer: Trainer = get_trainer(config["model"])(
          config, self.model, self.accelerator
      )
      self.do_train = config["do_train"]
      self.do_valid = config["do_valid"]
      self.do_test = config["do_test"]
      self.valid_result: Optional[ResultType] = None
      self.test_result: Optional[ResultType] = None
      if config["load_type"] == "resume":
          if config["resume_training"]:
              self.trainer.resume_checkpoint(config["model_path"])
          self.model.from_pretrained(config["model_path"])
  
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 5 for calling the Experiment obj's underlying member method code with example usage: obj._on_experiment_start(extended_config) without return types