code: def main(cfg) -> None:
      trainer = Trainer(strategy=NLPDDPStrategy(), **cfg.trainer)
  
      assert (
          cfg.trainer.devices * cfg.trainer.num_nodes
          == cfg.tensor_model_parallel_size
          * cfg.pipeline_model_parallel_size
      ), "devices * num_nodes should equal tensor_model_parallel_size * pipeline_model_parallel_size"
  
      if cfg.gpt_model_file:
          logging.info(f"Restoring model from {cfg.gpt_model_file}")
          save_restore_connector = NLPSaveRestoreConnector()
          if os.path.isdir(cfg.gpt_model_file):
              save_restore_connector.model_extracted_dir = (
                  cfg.gpt_model_file
              )
  
          pretrained_cfg = MegatronGPTModel.restore_from(
              restore_path=cfg.gpt_model_file,
              trainer=trainer,
              return_config=True,
              save_restore_connector=save_restore_connector,
          )
          pretrained_cfg = modify_pretrained_cfg(
              pretrained_cfg, trainer, cfg
          )
          model = MegatronGPTModel.restore_from(
              restore_path=cfg.gpt_model_file,
              trainer=trainer,
              override_config_path=pretrained_cfg,
              save_restore_connector=save_restore_connector,
              map_location=f"cuda:{trainer.local_rank}",  # map_location is needed for converted models
          )
      elif cfg.checkpoint_dir:
          logging.info(
              f"Restoring model from checkpoint_dir: {cfg.checkpoint_dir} with checkpoint name: {cfg.checkpoint_name}"
          )
          app_state = AppState()
          if (
              cfg.tensor_model_parallel_size > 1
              or cfg.pipeline_model_parallel_size > 1
          ):
              app_state.model_parallel_size = (
                  cfg.tensor_model_parallel_size
                  * cfg.pipeline_model_parallel_size
              )
              app_state.tensor_model_parallel_size = (
                  cfg.tensor_model_parallel_size
              )
              app_state.pipeline_model_parallel_size = (
                  cfg.pipeline_model_parallel_size
              )
              app_state.virtual_pipeline_model_parallel_size = (
                  cfg.virtual_pipeline_model_parallel_size
              )
              (
                  app_state.tensor_model_parallel_rank,
                  app_state.pipeline_model_parallel_rank,
                  app_state.model_parallel_size,
                  app_state.data_parallel_size,
                  app_state.pipeline_model_parallel_split_rank,
                  app_state.virtual_pipeline_model_parallel_rank,
              ) = fake_initialize_model_parallel(
                  world_size=app_state.model_parallel_size,
                  rank=trainer.global_rank,
                  tensor_model_parallel_size_=cfg.tensor_model_parallel_size,
                  pipeline_model_parallel_size_=cfg.pipeline_model_parallel_size,
                  virtual_pipeline_model_parallel_size_=cfg.virtual_pipeline_model_parallel_size,
              )
          checkpoint_path = inject_model_parallel_rank(
              os.path.join(cfg.checkpoint_dir, cfg.checkpoint_name)
          )
          pretrained_cfg = OmegaConf.load(cfg.hparams_file)
          pretrained_cfg = modify_pretrained_cfg(
              pretrained_cfg.cfg, trainer, cfg
          )
          with tempfile.NamedTemporaryFile(suffix=".yaml") as f:
              OmegaConf.save(config=pretrained_cfg, f=f.name)
              model = MegatronGPTModel.load_from_checkpoint(
                  checkpoint_path=checkpoint_path,
                  trainer=trainer,
                  hparams_file=f.name,
              )
      else:
          raise ValueError(
              "need at least a nemo file or checkpoint dir"
          )
  
      logging.info(
          "\n\n**************  Model configuration ***********"
      )
      logging.info(f"\n{OmegaConf.to_yaml(model.cfg)}")
  
      trainer.validate(model=model)
  
file_path: NeMo/examples/nlp/language_modeling/megatron_gpt_validate.py
imports: import os
  import tempfile
  from omegaconf import OmegaConf, open_dict
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
  from nemo.collections.nlp.modules.common.megatron.megatron_init import fake_initialize_model_parallel
  from nemo.collections.nlp.parts.nlp_overrides import MegatronHalfPrecisionPlugin, NLPDDPStrategy, NLPSaveRestoreConnector, PipelineMixedPrecisionPlugin
  from nemo.core.config import hydra_runner
  from nemo.utils import logging
  from nemo.utils.app_state import AppState
  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: main(cfg) without return types