code: def main(cfg) -> None:
      logging.info(
          "\n\n************** Experiment configuration ***********"
      )
      logging.info(f"\n{OmegaConf.to_yaml(cfg)}")
  
      megatron_amp_O2 = cfg.model.get("megatron_amp_O2", False)
      plugins = []
      strategy = NLPDDPStrategy(
          no_ddp_communication_hook=True,
          gradient_as_bucket_view=cfg.model.gradient_as_bucket_view,
          find_unused_parameters=False,
      )
      if cfg.trainer.precision in [
          16,
          "16",
          "bf16",
          "16-mixed",
          "bf16-mixed",
      ]:
          scaler = None
          if cfg.trainer.precision in [16, "16", "16-mixed"]:
              scaler = GradScaler(
                  init_scale=cfg.model.get(
                      "native_amp_init_scale", 2**32
                  ),
                  growth_interval=cfg.model.get(
                      "native_amp_growth_interval", 1000
                  ),
                  hysteresis=cfg.model.get("hysteresis", 2),
              )
              # MixedPrecisionPlugin in PTL >= 2.0 requires precision to be 16-mixed or bf16-mixed
              plugin_precision = "16-mixed"
          else:
              plugin_precision = "bf16-mixed"
          if megatron_amp_O2:
              plugins.append(
                  MegatronHalfPrecisionPlugin(
                      plugin_precision, device="cuda", scaler=scaler
                  )
              )
          else:
              plugins.append(
                  PipelineMixedPrecisionPlugin(
                      plugin_precision, device="cuda", scaler=scaler
                  )
              )
  
      if cfg.get("cluster_type", None) == "BCP":
          plugins.append(TorchElasticEnvironment())
  
      trainer = Trainer(
          plugins=plugins,
          strategy=strategy,
          **cfg.trainer,
          callbacks=[ModelSummary(max_depth=3), CustomProgressBar()],
      )
  
      exp_manager(trainer, cfg.exp_manager)
  
      # update resume from checkpoint found by exp_manager
      if cfg.model.resume_from_checkpoint is not None:
          trainer.ckpt_path = cfg.model.resume_from_checkpoint
  
      logging.info(
          f"Resuming training from checkpoint: {trainer.ckpt_path}"
      )
  
      model = MegatronBARTModel(cfg.model, trainer)
      trainer.fit(model)
  
file_path: NeMo/examples/nlp/language_modeling/megatron_bart_pretraining.py
imports: from omegaconf.omegaconf import OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from pytorch_lightning.callbacks import ModelSummary
  from pytorch_lightning.plugins.environments import TorchElasticEnvironment
  from pytorch_lightning.trainer.connectors.checkpoint_connector import _CheckpointConnector
  from nemo.collections.nlp.models.language_modeling.megatron_bart_model import MegatronBARTModel
  from nemo.collections.nlp.parts.nlp_overrides import CustomProgressBar, GradScaler, MegatronHalfPrecisionPlugin, NLPDDPStrategy, PipelineMixedPrecisionPlugin
  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