code: def main(cfg):
      """
      Training hybrid ASR-TTS model using text-only data and/or audio-text pairs.
      Provide ASR model config, add options related to TTS and text-only data.
      """
      logging.info(f"Hydra config: {OmegaConf.to_yaml(cfg)}")
      OmegaConf.resolve(cfg)
  
      trainer = pl.Trainer(**cfg.trainer)
      exp_manager(trainer, cfg.get("exp_manager", None))
  
      asr_model = ASRWithTTSModel.from_asr_config(
          asr_cfg=cfg.model,
          asr_model_type=cfg.asr_model_type,
          tts_model_path=cfg.tts_model_path,
          enhancer_model_path=cfg.get("enhancer_model_path", None),
          trainer=trainer,
      )
  
      # Initialize the weights of the model from another model, if provided via config
      asr_model.maybe_init_from_pretrained_checkpoint(cfg)
  
      trainer.fit(asr_model)
  
      if (
          hasattr(cfg.model, "test_ds")
          and cfg.model.test_ds.manifest_filepath is not None
      ):
          if asr_model.prepare_test(trainer):
              trainer.test(asr_model)
  
imports: import pytorch_lightning
  from omegaconf import OmegaConf
  from nemo.collections.asr.models.hybrid_asr_tts_models import ASRWithTTSModel
  from nemo.core.config import hydra_runner
  from nemo.utils import logging
  from nemo.utils.exp_manager import exp_manager
label_desc: Use image node_id 1 for calling a global function with example usage: main(cfg) without return types