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 if megatron_amp_O2 else False,
          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),
              )
              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(
                  MixedPrecisionPlugin(
                      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=[CustomProgressBar()],
      )
  
      exp_manager(trainer, cfg.exp_manager)
  
      # resume_from_checkpoint = uninject_model_parallel_rank(resume_from_checkpoint)
      logging.info(
          f"Resuming training from checkpoint: {trainer.ckpt_path}"
      )
  
      # load existing nemo retro model
      if cfg.get("restore_from_path", None) is not None:
          save_restore_connector = NLPSaveRestoreConnector()
          if os.path.isdir(cfg.restore_from_path):
              save_restore_connector.model_extracted_dir = (
                  cfg.restore_from_path
              )
          model = MegatronRetrievalModel.restore_from(
              restore_path=cfg.restore_from_path,
              trainer=trainer,
              override_config_path=cfg.model,
              save_restore_connector=save_restore_connector,
              strict=False,
          )
      else:
          model = MegatronRetrievalModel(cfg.model, trainer)
  
      trainer.fit(model)
  
file_path: NeMo/examples/nlp/language_modeling/megatron_retro_pretraining.py
imports: import os
  from omegaconf.omegaconf import OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from pytorch_lightning.plugins.environments import TorchElasticEnvironment
  from pytorch_lightning.plugins.precision import MixedPrecisionPlugin
  from pytorch_lightning.trainer.connectors.checkpoint_connector import _CheckpointConnector
  from nemo.collections.nlp.models.language_modeling.megatron_retrieval_model import MegatronRetrievalModel
  from nemo.collections.nlp.modules.common.megatron.megatron_init import initialize_model_parallel_for_nemo
  from nemo.collections.nlp.parts.nlp_overrides import CustomProgressBar, GradScaler, MegatronHalfPrecisionPlugin, NLPDDPStrategy, 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