code: def get_args():
      parser = ArgumentParser()
      parser.add_argument(
          "--checkpoint_folder",
          type=str,
          default=None,
          required=True,
          help="Path to PTL checkpoints saved during training. Ex: /raid/nemo_experiments/megatron_gpt/checkpoints",
      )
      parser.add_argument(
          "--checkpoint_name",
          type=str,
          default=None,
          required=True,
          help="Name of checkpoint to be used. Ex: megatron_gpt--val_loss=6.34-step=649-last.ckpt",
      )
  
      parser.add_argument(
          "--hparams_file",
          type=str,
          default=None,
          required=False,
          help="Path config for restoring. It's created during training and may need to be modified during restore if restore environment is different than training. Ex: /raid/nemo_experiments/megatron_gpt/hparams.yaml",
      )
      parser.add_argument(
          "--nemo_file_path",
          type=str,
          default=None,
          required=True,
          help="Path to output .nemo file.",
      )
      parser.add_argument(
          "--gpus_per_node", type=int, required=True, default=None
      )
      parser.add_argument(
          "--tensor_model_parallel_size",
          type=int,
          required=True,
          default=None,
      )
      parser.add_argument(
          "--pipeline_model_parallel_size",
          type=int,
          required=True,
          default=None,
      )
      parser.add_argument(
          "--pipeline_model_parallel_split_rank",
          type=int,
          required=False,
          default=None,
          help="If pipeline parallel size > 1, this is the rank at which the encoder ends and the decoder begins.",
      )
      parser.add_argument(
          "--model_type",
          type=str,
          required=True,
          default="gpt",
          choices=["gpt", "sft", "t5", "bert", "nmt", "bart", "retro"],
      )
      parser.add_argument(
          "--local_rank",
          type=int,
          required=False,
          default=os.getenv("LOCAL_RANK", -1),
      )
      parser.add_argument(
          "--bcp", action="store_true", help="Whether on BCP platform"
      )
      parser.add_argument(
          "--precision",
          type=str,
          required=False,
          default="16-mixed",
          choices=["32-true", "16-mixed", "bf16-mixed"],
          help="Precision value for the trainer that matches with precision of the ckpt",
      )
  
      args = parser.parse_args()
      return args
  
imports: import dis
  import os
  from argparse import ArgumentParser
  import torch
  from genericpath import isdir
  from megatron.core import parallel_state
  from omegaconf import OmegaConf, open_dict
  from pytorch_lightning.plugins.environments import TorchElasticEnvironment
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.nlp.models.language_modeling.megatron_bart_model import MegatronBARTModel
  from nemo.collections.nlp.models.language_modeling.megatron_bert_model import MegatronBertModel
  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.models.language_modeling.megatron_retrieval_model import MegatronRetrievalModel
  from nemo.collections.nlp.models.language_modeling.megatron_t5_model import MegatronT5Model
  from nemo.collections.nlp.models.machine_translation.megatron_nmt_model import MegatronNMTModel
  from nemo.collections.nlp.parts.nlp_overrides import GradScaler, NLPDDPStrategy, NLPSaveRestoreConnector, PipelineMixedPrecisionPlugin
  from nemo.utils import AppState, logging
  from nemo.utils.distributed import initialize_distributed
  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: get_args() and returns: args