code: def get_pretrained_roberta(
      model_name: str = "fairseq_roberta_base",
      root: str = get_model_zoo_home_dir(),
      load_backbone: bool = True,
      load_mlm: bool = False,
  ) -> Tuple[CN, HuggingFaceByteBPETokenizer, str, str]:
      """Get the pretrained RoBERTa weights
  
      Parameters
      ----------
      model_name
          The name of the RoBERTa model.
      root
          The downloading root
      load_backbone
          Whether to load the weights of the backbone network
      load_mlm
          Whether to load the weights of MLM
  
      Returns
      -------
      cfg
          Network configuration
      tokenizer
          The HuggingFaceByteBPETokenizer
      params_path
          Path to the parameters
      mlm_params_path
          Path to the parameter that includes both the backbone and the MLM
      """
      assert (
          model_name in PRETRAINED_URL
      ), "{} is not found. All available are {}".format(
          model_name, list_pretrained_roberta()
      )
      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"]
      mlm_params_path = PRETRAINED_URL[model_name]["mlm_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_mlm and mlm_params_path is not None:
          local_mlm_params_path = download(
              url=get_repo_model_zoo_url() + mlm_params_path,
              path=os.path.join(root, mlm_params_path),
              sha1_hash=FILE_STATS[mlm_params_path],
          )
      else:
          local_mlm_params_path = None
      do_lower = (
          True
          if "lowercase" in PRETRAINED_URL[model_name]
          and PRETRAINED_URL[model_name]["lowercase"]
          else False
      )
      tokenizer = HuggingFaceByteBPETokenizer(
          merges_file=local_paths["merges"],
          vocab_file=local_paths["vocab"],
          lowercase=do_lower,
      )
      if cfg is None:
          cfg = RobertaModel.get_cfg().clone_merge(local_paths["cfg"])
      return cfg, tokenizer, local_params_path, local_mlm_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 ..op import select_vectors_by_position
  from ..base import get_model_zoo_home_dir, get_repo_model_zoo_url, get_model_zoo_checksum_dir
  from ..layers import PositionalEmbedding, get_activation
  from ..utils.misc import download, load_checksum_stats
  from ..utils.registry import Registry
  from .transformer import TransformerEncoderLayer
  from ..initializer import TruncNorm
  from ..utils.config import CfgNode
  from ..attention_cell import gen_self_attn_mask
  from ..data.tokenizers import HuggingFaceByteBPETokenizer
label_desc: Use image node_id 4 for calling a global function with example usage: get_pretrained_roberta(model_name, root, load_backbone, load_mlm) and returns: cfg,  tokenizer,  local_params_path,  local_mlm_params_path