code: def main(cfg) -> None:
      # trainer required for restoring model parallel models
      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"
  
      app_state = AppState()
      app_state.model_parallel_size = (
          cfg.tensor_model_parallel_size
          * cfg.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,
          pipeline_model_parallel_split_rank_=cfg.pipeline_model_parallel_split_rank,
      )
  
      if cfg.model_file is not None:
          if not os.path.exists(cfg.model_file):
              raise ValueError(
                  f"Model file {cfg.model_file} does not exist"
              )
          pretrained_cfg = MegatronNMTModel.restore_from(
              cfg.model_file, trainer=trainer, return_config=True
          )
          OmegaConf.set_struct(pretrained_cfg, True)
          with open_dict(pretrained_cfg):
              pretrained_cfg.precision = trainer.precision
          model = MegatronNMTModel.restore_from(
              restore_path=cfg.model_file,
              trainer=trainer,
              save_restore_connector=NLPSaveRestoreConnector(),
              override_config_path=pretrained_cfg,
          )
      elif cfg.checkpoint_dir is not None:
          checkpoint_path = inject_model_parallel_rank(
              os.path.join(cfg.checkpoint_dir, cfg.checkpoint_name)
          )
          model = MegatronNMTModel.load_from_checkpoint(
              checkpoint_path,
              hparams_file=cfg.hparams_file,
              trainer=trainer,
          )
      else:
          raise ValueError(
              "need at least a nemo file or checkpoint dir"
          )
  
      model.freeze()
  
      logging.info(f"Translating: {cfg.srctext}")
      src_text = []
      translations = []
      with open(cfg.srctext, "r") as src_f, open(
          cfg.tgtout, "w"
      ) as tgt_f:
          for line in src_f:
              src_text.append(line.strip())
              if len(src_text) == cfg.batch_size:
                  translations = model.translate(
                      text=src_text,
                      source_lang=cfg.source_lang,
                      target_lang=cfg.target_lang,
                  )
                  for translation in translations:
                      tgt_f.write(translation + "\n")
                  src_text = []
          if len(src_text) > 0:
              translations = model.translate(
                  text=src_text,
                  source_lang=cfg.source_lang,
                  target_lang=cfg.target_lang,
              )
              for translation in translations:
                  tgt_f.write(translation + "\n")
  
file_path: NeMo/examples/nlp/machine_translation/nmt_transformer_infer_megatron.py
imports: import os
  from omegaconf.omegaconf import OmegaConf, open_dict
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.nlp.models.machine_translation.megatron_nmt_model import MegatronNMTModel
  from nemo.collections.nlp.modules.common.megatron.megatron_init import fake_initialize_model_parallel
  from nemo.collections.nlp.modules.common.megatron.utils import ApexGuardDefaults
  from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy, NLPSaveRestoreConnector
  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 1 for calling a global function with example usage: main(cfg) without return types