code: def convert_roberta_prelayernorm_checkpoint_to_pytorch(
      checkpoint_repo: str, pytorch_dump_folder_path: str
  ):
      """
      Copy/paste/tweak roberta_prelayernorm's weights to our BERT structure.
      """
      # convert configuration
      config = RobertaPreLayerNormConfig.from_pretrained(
          checkpoint_repo,
          architectures=["RobertaPreLayerNormForMaskedLM"],
      )
  
      # convert state_dict
      original_state_dict = torch.load(
          hf_hub_download(
              repo_id=checkpoint_repo, filename="pytorch_model.bin"
          )
      )
      state_dict = {}
      for tensor_key, tensor_value in original_state_dict.items():
          # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
          if tensor_key.startswith("roberta."):
              tensor_key = (
                  "roberta_prelayernorm."
                  + tensor_key[len("roberta.") :]
              )
  
          # The original implementation contains weights which are not used, remove them from the state_dict
          if tensor_key.endswith(
              ".self.LayerNorm.weight"
          ) or tensor_key.endswith(".self.LayerNorm.bias"):
              continue
  
          state_dict[tensor_key] = tensor_value
  
      model = RobertaPreLayerNormForMaskedLM.from_pretrained(
          pretrained_model_name_or_path=None,
          config=config,
          state_dict=state_dict,
      )
      model.save_pretrained(pytorch_dump_folder_path)
  
      # convert tokenizer
      tokenizer = AutoTokenizer.from_pretrained(checkpoint_repo)
      tokenizer.save_pretrained(pytorch_dump_folder_path)
  
imports: import argparse
  import torch
  from huggingface_hub import hf_hub_download
  from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
  from transformers.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: convert_roberta_prelayernorm_checkpoint_to_pytorch(checkpoint_repo, pytorch_dump_folder_path) without return types