code: def process_args(args, config=None):
      """Process parsed comand line arguments and initialize the config if not provided.
      Args:
          args (argparse.Namespace or dict like): Parsed input arguments.
          config (Coqpit): Model config. If none, it is generated from `args`. Defaults to None.
      Returns:
          c (TTS.utils.io.AttrDict): Config paramaters.
          out_path (str): Path to save models and logging.
          audio_path (str): Path to save generated test audios.
          c_logger (TTS.utils.console_logger.ConsoleLogger): Class that does
              logging to the console.
          dashboard_logger (WandbLogger or TensorboardLogger): Class that does the dashboard Logging
      TODO:
          - Interactive config definition.
      """
      if isinstance(args, tuple):
          args, coqpit_overrides = args
      if args.continue_path:
          # continue a previous training from its output folder
          experiment_path = args.continue_path
          args.config_path = os.path.join(
              args.continue_path, "config.json"
          )
          args.restore_path, best_model = get_last_checkpoint(
              args.continue_path
          )
          if not args.best_path:
              args.best_path = best_model
      # init config if not already defined
      if config is None:
          if args.config_path:
              # init from a file
              config = load_config(args.config_path)
          else:
              # init from console args
              from TTS.config.shared_configs import (
                  BaseTrainingConfig,
              )  # pylint: disable=import-outside-toplevel
  
              config_base = BaseTrainingConfig()
              config_base.parse_known_args(coqpit_overrides)
              config = register_config(config_base.model)()
      # override values from command-line args
      config.parse_known_args(coqpit_overrides, relaxed_parser=True)
      experiment_path = args.continue_path
      if not experiment_path:
          experiment_path = get_experiment_folder_path(
              config.output_path, config.run_name
          )
      audio_path = os.path.join(experiment_path, "test_audios")
      config.output_log_path = experiment_path
      # setup rank 0 process in distributed training
      dashboard_logger = None
      if args.rank == 0:
          new_fields = {}
          if args.restore_path:
              new_fields["restore_path"] = args.restore_path
          new_fields["github_branch"] = get_git_branch()
          # if model characters are not set in the config file
          # save the default set to the config file for future
          # compatibility.
          if config.has("characters") and config.characters is None:
              used_characters = parse_symbols()
              new_fields["characters"] = used_characters
          copy_model_files(config, experiment_path, new_fields)
          dashboard_logger = logger_factory(config, experiment_path)
      c_logger = ConsoleLogger()
      return (
          config,
          experiment_path,
          audio_path,
          c_logger,
          dashboard_logger,
      )
  
file_path: TTS/TTS/encoder/utils/training.py
imports: import os
  from dataclasses import dataclass, field
  from coqpit import Coqpit
  from trainer import TrainerArgs, get_last_checkpoint
  from trainer.io import copy_model_files
  from trainer.logging import logger_factory
  from trainer.logging.console_logger import ConsoleLogger
  from TTS.config import load_config, register_config
  from TTS.tts.utils.text.characters import parse_symbols
  from TTS.utils.generic_utils import get_experiment_folder_path, get_git_branch
label_desc: Use image node_id 2 for calling a global function with example usage: process_args(args, config) and returns: config,  experiment_path,  audio_path,  c_logger,  dashboard_logger