code: def load_pretrained_model(
      model_name_or_path_or_checkpoint, *args, **kwargs
  ):
      # If this is a file, then load this directly else download and load
      if PathManager.isfile(model_name_or_path_or_checkpoint):
          return _load_pretrained_checkpoint(
              model_name_or_path_or_checkpoint, args, kwargs
          )
      else:
          return _load_pretrained_model(
              model_name_or_path_or_checkpoint, args, kwargs
          )
  
imports: import glob
  import importlib
  import logging
  import os
  import sys
  import warnings
  from typing import Any, Dict
  import torch
  from mmf.common.registry import registry
  from mmf.utils.checkpoint_updater import get_pretrained_state_mapping_checkpoint
  from mmf.utils.configuration import get_mmf_env, load_yaml
  from mmf.utils.distributed import is_main, is_xla, open_if_main, synchronize
  from mmf.utils.download import download_pretrained_model
  from mmf.utils.file_io import PathManager
  from mmf.utils.general import get_current_device, updir
  from mmf.utils.xla import save_xla_ckpt
  from omegaconf import OmegaConf
label_desc: Use image node_id 7 for calling a global function with example usage: load_pretrained_model(model_name_or_path_or_checkpoint) and returns: _load_pretrained_checkpoint, _load_pretrained_model