code: def get_pretrained_gpt2(
      model_name: str = "gpt2_124M",
      root: str = get_model_zoo_home_dir(),
      load_backbone: bool = True,
      load_lm: bool = False,
  ) -> Tuple[CN, HuggingFaceByteBPETokenizer, str, str]:
      """Get the pretrained GPT-2 weights
  
      Parameters
      ----------
      model_name
          The name of the GPT-2 model.
      root
          The downloading root
      load_backbone
          Whether to load the weights of the backbone network
      load_lm
          Whether to load the weights of LM
  
      Returns
      -------
      cfg
          Network configuration
      tokenizer
          The HuggingFaceByteBPETokenizer
      params_path
          Path to the parameters
      lm_params_path
          Path to the parameter that includes both the backbone and the LM
      """
      assert (
          model_name in PRETRAINED_URL
      ), "{} is not found. All available are {}".format(
          model_name, list_pretrained_gpt2()
      )
      cfg_path = PRETRAINED_URL[model_name]["cfg"]
      if isinstance(cfg_path, CN):
          cfg = cfg_path
      else:
          cfg = None
      merges_path = PRETRAINED_URL[model_name]["merges"]
      vocab_path = PRETRAINED_URL[model_name]["vocab"]
      params_path = PRETRAINED_URL[model_name]["params"]
      lm_params_path = PRETRAINED_URL[model_name]["lm_params"]
  
      local_paths = dict()
      download_jobs = [("vocab", vocab_path), ("merges", merges_path)]
      if cfg is None:
          download_jobs.append(("cfg", cfg_path))
      for k, path in download_jobs:
          local_paths[k] = download(
              url=get_repo_model_zoo_url() + path,
              path=os.path.join(root, path),
              sha1_hash=FILE_STATS[path],
          )
      if load_backbone:
          local_params_path = download(
              url=get_repo_model_zoo_url() + params_path,
              path=os.path.join(root, params_path),
              sha1_hash=FILE_STATS[params_path],
          )
      else:
          local_params_path = None
      if load_lm and lm_params_path is not None:
          local_lm_params_path = download(
              url=get_repo_model_zoo_url() + lm_params_path,
              path=os.path.join(root, lm_params_path),
              sha1_hash=FILE_STATS[lm_params_path],
          )
      else:
          local_lm_params_path = None
  
      tokenizer = HuggingFaceByteBPETokenizer(
          merges_file=local_paths["merges"],
          vocab_file=local_paths["vocab"],
      )
      if cfg is None:
          cfg = GPT2Model.get_cfg().clone_merge(local_paths["cfg"])
      return cfg, tokenizer, local_params_path, local_lm_params_path
  
imports: import os
  from typing import Tuple
  import mxnet
  from mxnet import use_np, np, npx
  from mxnet.gluon import HybridBlock, nn
  from .base import BACKBONE_REGISTRY
  from ..base import get_model_zoo_home_dir, get_repo_model_zoo_url, get_model_zoo_checksum_dir
  from ..utils.config import CfgNode
  from ..utils.misc import load_checksum_stats, download
  from ..utils.registry import Registry
  from ..initializer import TruncNorm
  from ..attention_cell import MultiHeadAttentionCell
  from ..layers import get_activation, PositionalEmbedding
  from ..data.tokenizers import HuggingFaceByteBPETokenizer
label_desc: Use image node_id 6 for calling a global function with example usage: get_pretrained_gpt2(model_name, root, load_backbone, load_lm) and returns: cfg,  tokenizer,  local_params_path,  local_lm_params_path