code: def main(cfg) -> None:
      # trainer required for restoring model parallel models
      trainer = Trainer(strategy=NLPDDPStrategy(), **cfg.trainer)
  
      if (
          cfg.tensor_model_parallel_size < 0
          or cfg.pipeline_model_parallel_size < 0
          or cfg.get("pipeline_model_parallel_split_rank", -1) < 0
      ):
          model_config = MegatronGPTModel.restore_from(
              restore_path=cfg.gpt_model_file,
              trainer=trainer,
              return_config=True,
          )
  
          with open_dict(cfg):
              cfg.tensor_model_parallel_size = model_config.get(
                  "tensor_model_parallel_size", 1
              )
              cfg.pipeline_model_parallel_size = model_config.get(
                  "pipeline_model_parallel_size", 1
              )
              cfg.pipeline_model_parallel_split_rank = model_config.get(
                  "pipeline_model_parallel_split_rank", 0
              )
  
      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:
          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,
          )
          OmegaConf.set_struct(pretrained_cfg, True)
          with open_dict(pretrained_cfg):
              pretrained_cfg.sequence_parallel = False
              pretrained_cfg.activations_checkpoint_granularity = None
              pretrained_cfg.activations_checkpoint_method = None
              pretrained_cfg.precision = trainer.precision
          model = MegatronGPTModel.restore_from(
              restore_path=cfg.gpt_model_file,
              trainer=trainer,
              override_config_path=pretrained_cfg,
              map_location=torch.device("cpu"),
              save_restore_connector=save_restore_connector,
          )
      elif cfg.checkpoint_dir:
          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.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,
              )
          checkpoint_path = inject_model_parallel_rank(
              os.path.join(cfg.checkpoint_dir, cfg.checkpoint_name)
          )
          model = MegatronGPTModel.load_from_checkpoint(
              checkpoint_path,
              hparams_file=cfg.hparams_file,
              trainer=trainer,
          )
      else:
          raise ValueError(
              "need at least a nemo file or checkpoint dir"
          )
  
      # load the lora weights on cpu for all ranks of the lora model
      lora_weights, lora_model_cfg = load_lora(
          cfg.lora_model_path, model.cfg.tensor_model_parallel_size
      )
  
      # merge the lora weights with the base model, for this current rank.
      merged_weights = merge(
          model.state_dict(),
          lora_weights,
          tp=model.cfg.tensor_model_parallel_size,
          num_layers=model.cfg.num_layers,
          curr_rank=model.global_rank,
          mcore=model.mcore_gpt,
      )
  
      # load the merged_weights back into the base model, for this current rank.
      if model.cfg.megatron_amp_O2:
          merged_weights = fix_for_O2(merged_weights)
      model.load_state_dict(merged_weights)
  
      # Going to go through the motions of inference to force PTL to run subprocess for loading all base model's ranks.
      input = "Context: In 2004, philosopher and psychologist Michel ter Hark (Groningen, The Netherlands) published a book, called Popper, Otto Selz and the rise of evolutionary epistemology, in which he claimed that Popper took some of his ideas from his tutor, the German psychologist Otto Selz. Selz never published his ideas, partly because of the rise of Nazism, which forced him to quit his work in 1933, and the prohibition of referring to Selz' work. Popper, the historian of ideas and his scholarship, is criticised in some academic quarters for his rejection of Plato, Hegel and Marx. Question: Who claimed Otto Selz deserved credit for ideas published by Popper? Answer:"
      ds = RequestDataSet([input])
      request_dl = DataLoader(dataset=ds, batch_size=1)
      config = {
          "greedy": True,
          "compute_logprob": False,
          "tokens_to_generate": 5,
          "add_BOS": False,
      }
      model.set_inference_config(config)
      response = trainer.predict(model, request_dl)
      print(response)
  
      with open_dict(model.cfg):
          model.cfg.restore_from_path = cfg.merged_model_path
          model.cfg.data = lora_model_cfg.data
          model.cfg.target = f"{MegatronGPTSFTModel.__module__}.{MegatronGPTSFTModel.__name__}"
  
      model.save_to(cfg.merged_model_path)
      logging.info(f"saved merged model to {cfg.merged_model_path}")
  
imports: import os
  import tempfile
  from typing import Any, Dict
  import torch
  from omegaconf import OmegaConf, open_dict
  from pytorch_lightning.trainer.trainer import Trainer
  from torch.utils.data import DataLoader, Dataset
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_sft_model import MegatronGPTSFTModel
  from nemo.collections.nlp.modules.common.megatron.megatron_init import fake_initialize_model_parallel
  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 4 for calling a global function with example usage: main(cfg) without return types