code: def main():
      """Run `tts` model training directly by a `config.json` file."""
      # init trainer args
      train_args = TrainTTSArgs()
      parser = train_args.init_argparse(arg_prefix="")
  
      # override trainer args from comman-line args
      args, config_overrides = parser.parse_known_args()
      train_args.parse_args(args)
  
      # load config.json and register
      if args.config_path or args.continue_path:
          if args.config_path:
              # init from a file
              config = load_config(args.config_path)
              if len(config_overrides) > 0:
                  config.parse_known_args(
                      config_overrides, relaxed_parser=True
                  )
          elif args.continue_path:
              # continue from a prev experiment
              config = load_config(
                  os.path.join(args.continue_path, "config.json")
              )
              if len(config_overrides) > 0:
                  config.parse_known_args(
                      config_overrides, relaxed_parser=True
                  )
          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(config_overrides)
              config = register_config(config_base.model)()
  
      # load training samples
      train_samples, eval_samples = load_tts_samples(
          config.datasets,
          eval_split=True,
          eval_split_max_size=config.eval_split_max_size,
          eval_split_size=config.eval_split_size,
      )
  
      # init the model from config
      model = setup_model(config, train_samples + eval_samples)
  
      # init the trainer and 🚀
      trainer = Trainer(
          train_args,
          model.config,
          config.output_path,
          model=model,
          train_samples=train_samples,
          eval_samples=eval_samples,
          parse_command_line_args=False,
      )
      trainer.fit()
  
file_path: TTS/TTS/bin/train_tts.py
imports: import os
  from dataclasses import dataclass, field
  from trainer import Trainer, TrainerArgs
  from TTS.config import load_config, register_config
  from TTS.tts.datasets import load_tts_samples
  from TTS.tts.models import setup_model
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types