code: def load_from_nemo(
      cls,
      cfg,
      trainer,
      retro_cfg,
      modify_confg_fn,
      save_restore_connector,
  ):
      retro_cfg = modify_confg_fn(retro_cfg, cfg, add_cfg_to_tree=False)
      model = cls.restore_from(
          restore_path=cfg.model.restore_path,
          trainer=trainer,
          override_config_path=retro_cfg,
          save_restore_connector=save_restore_connector,
      )
      return model
  
file_path: NeMo/examples/nlp/language_modeling/megatron_retro_fine_tune.py
imports: import datetime
  import os
  from omegaconf.omegaconf import OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from pytorch_lightning.callbacks.timer import Timer
  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_retro_fine_tune_model import MegatronRetroFinetuneModel
  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 StatelessTimer, exp_manager
label_desc: Use image node_id 2 for calling a global function with example usage: load_from_nemo(cls, cfg, trainer, retro_cfg, modify_confg_fn, save_restore_connector) and returns: model