code: def main(cfg) -> None:
      logging.info(
          "\n\n************** Experiment configuration ***********"
      )
      logging.info(f"\n{OmegaConf.to_yaml(cfg)}")
  
      assert (
          cfg.trainer.devices * cfg.trainer.num_nodes
      ) * cfg.model.micro_batch_size == cfg.model.global_batch_size, (
          "Gradient accumulation is not supported in CLIP yet."
      )
  
      trainer = MegatronTrainerBuilder(cfg).create_trainer()
      exp_manager(trainer, cfg.exp_manager)
  
      model = MegatronContentFilteringModel.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)
  
      if "save_path" in cfg.model:
          logging.info(f"Saving model to path: {cfg.model.save_path}")
          model.save_to(cfg.model.save_path)
  
imports: from omegaconf.omegaconf import OmegaConf
  from nemo.collections.multimodal.models.vision_language_foundation.megatron_nsfw_clip_models import MegatronContentFilteringModel
  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