code: def get_args():
      parser = ArgumentParser()
      parser.add_argument(
          "--ckpt_path",
          type=str,
          default=None,
          required=True,
          help="Path to checkpoint.",
      )
  
      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=False, default=1
      )
      parser.add_argument(
          "--tensor_model_parallel_size",
          type=int,
          required=False,
          default=1,
      )
      parser.add_argument(
          "--pipeline_model_parallel_size",
          type=int,
          required=False,
          default=1,
      )
      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(
          "--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(
          "--model_type",
          type=str,
          required=False,
          default="stable_diffusion",
      )
      parser.add_argument(
          "--nemo_clip_path",
          type=str,
          required=False,
          help="Path to clip ckpt file in .nemo format",
      )
  
      args = parser.parse_args()
      return args
  
file_path: NeMo/examples/multimodal/text_to_image/convert_hf_ckpt_to_nemo.py
imports: import os
  import tempfile
  from argparse import ArgumentParser
  import torch
  from lightning_fabric.utilities.cloud_io import _load
  from omegaconf import OmegaConf
  from pytorch_lightning.plugins.environments import TorchElasticEnvironment
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.multimodal.models.text_to_image.controlnet.controlnet import MegatronControlNet
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import MegatronLatentDiffusion
  from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector
  from nemo.utils import AppState, logging
  from nemo.utils.distributed import initialize_distributed
label_desc: Use image node_id 1 for calling a global function with example usage: get_args() and returns: args