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,  # we don't use DDP for async grad allreduce
          gradient_as_bucket_view=cfg.model.gradient_as_bucket_view,
          find_unused_parameters=False,
      )
      if cfg.trainer.precision in [
          16,
          "16",
          "16-mixed",
          "bf16",
          "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(
                      precision=plugin_precision,
                      device="cuda",
                      scaler=scaler,
                  )
              )
          else:
              plugins.append(
                  PipelineMixedPrecisionPlugin(
                      precision=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}"
      )
  
      if (
          hasattr(cfg.model, "pretrained_model_path")
          and cfg.model.pretrained_model_path is not None
      ):
          pretrained_cfg = MegatronT5Model.restore_from(
              cfg.model.pretrained_model_path,
              trainer=trainer,
              return_config=True,
          )
          OmegaConf.set_struct(pretrained_cfg, True)
          with open_dict(pretrained_cfg):
              # Override data from T5 to Prefix-LM
              encoder_seq_length = pretrained_cfg.data.seq_length
              decoder_seq_length = (
                  pretrained_cfg.data.seq_length
              )  # Set decoder seq length to be enoder seq length for prefix-lm
              pretrained_cfg.data = cfg.model.data
              pretrained_cfg.data.seq_length = encoder_seq_length
              pretrained_cfg.data.seq_length_dec = (
                  decoder_seq_length - 1
              )  # -1 is to account for the addition of <bos> and <eos> and right shifting to create targets.
  
              # Override fusion params.
              pretrained_cfg.masked_softmax_fusion = (
                  cfg.model.masked_softmax_fusion
              )
              pretrained_cfg.bias_dropout_add_fusion = (
                  cfg.model.bias_dropout_add_fusion
              )
              pretrained_cfg.bias_gelu_fusion = (
                  cfg.model.bias_gelu_fusion
              )
  
              # Override dropout
              if cfg.model.hidden_dropout is not None:
                  pretrained_cfg.hidden_dropout = (
                      cfg.model.hidden_dropout
                  )
  
              if cfg.model.attention_dropout is not None:
                  pretrained_cfg.attention_dropout = (
                      cfg.model.attention_dropout
                  )
  
              # Override precision
              pretrained_cfg.precision = (
                  trainer.precision
              )  # Set above from trainer.precision
  
              # Override micro/global batch
              pretrained_cfg.micro_batch_size = (
                  cfg.model.micro_batch_size
              )
              pretrained_cfg.global_batch_size = (
                  cfg.model.global_batch_size
              )
  
              # O2 AMP
              pretrained_cfg.megatron_amp_O2 = cfg.model.get(
                  "megatron_amp_O2", False
              )
  
              # Optimizer overrides.
              pretrained_cfg.optim = cfg.model.optim
  
          model = MegatronT5Model.restore_from(
              cfg.model.pretrained_model_path,
              trainer=trainer,
              override_config_path=pretrained_cfg,
              save_restore_connector=NLPSaveRestoreConnector(),
          )
      else:
          raise ValueError(
              f"No pretrained model path specified or does not exist {cfg.model.pretrained_model_path}"
          )
  
      trainer.fit(model)
  
file_path: NeMo/examples/nlp/language_modeling/megatron_t5_lm_adaptation_finetune.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_t5_model import MegatronT5Model
  from nemo.collections.nlp.parts.nlp_overrides import CustomProgressBar, GradScaler, MegatronHalfPrecisionPlugin, NLPDDPStrategy, NLPSaveRestoreConnector, 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