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_logger(config: Config) -> logging.Logger:
      # logger initialization
      init_logger(
          filename=config["filename"],
          log_level=config["state"],
          enabled=config["_is_local_main_process"],
          saved_dir=config["saved_dir"],
      )
      logger = get_logger(__name__)
      logger.info(config)
  
      return logger
  
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 3 for calling the Experiment obj's underlying member method code with example usage: obj.init_logger(config) and returns: logger