code: def load_from_nemo(cls, cfg, trainer, t5_cfg, modify_confg_fn):
      t5_cfg = modify_confg_fn(t5_cfg, cfg, add_cfg_to_tree=False)
      model = cls.restore_from(
          restore_path=cfg.model.restore_from_path,
          trainer=trainer,
          override_config_path=t5_cfg,
          save_restore_connector=NLPSaveRestoreConnector(),
      )
      return model
  
imports: import os
  import tempfile
  import torch.multiprocessing
  from omegaconf.omegaconf import OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from pytorch_lightning.plugins.environments import TorchElasticEnvironment
  from pytorch_lightning.trainer.connectors.checkpoint_connector import _CheckpointConnector
  from nemo.collections.nlp.models.language_modeling.megatron_glue_model import MegatronT5GLUEModel
  from nemo.collections.nlp.models.language_modeling.megatron_t0_model import MegatronT0Model
  from nemo.collections.nlp.models.language_modeling.megatron_t5_sft_model import MegatronT5SFTModel
  from nemo.collections.nlp.modules.common.megatron.megatron_init import fake_initialize_model_parallel
  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 AppState, logging
  from nemo.utils.exp_manager import exp_manager
  from nemo.utils.model_utils import inject_model_parallel_rank
label_desc: Use image node_id 2 for calling a global function with example usage: load_from_nemo(cls, cfg, trainer, t5_cfg, modify_confg_fn) and returns: model