code: def main(cfg) -> None:
      logging.info(
          "\n\n************** Experiment configuration ***********"
      )
      logging.info(f"\n{OmegaConf.to_yaml(cfg)}")
  
      trainer = MegatronTrainerBuilder(cfg).create_trainer()
      exp_manager(trainer, cfg.exp_manager)
  
      if cfg.model.restore_from_path is None:
          model = MegatronNevaModel(cfg.model, trainer)
      else:
          model = MegatronNevaModel.restore_from(
              restore_path=cfg.model.restore_from_path,
              trainer=trainer,
              override_config_path=cfg.model,
              save_restore_connector=NLPSaveRestoreConnector(),
              strict=False,
          )
  
      trainer.fit(model)
  
file_path: NeMo/examples/multimodal/multimodal_llm/neva/neva_finetune.py
imports: import torch.multiprocessing
  from omegaconf.omegaconf import OmegaConf
  from nemo.collections.multimodal.models.multimodal_llm.neva.neva_model import MegatronNevaModel
  from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronTrainerBuilder
  from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector
  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