code: def load_original_entity_vocab(entity_vocab_path):
      SPECIAL_TOKENS = ["[MASK]", "[PAD]", "[UNK]"]
  
      data = [json.loads(line) for line in open(entity_vocab_path)]
  
      new_mapping = {}
      for entry in data:
          entity_id = entry["id"]
          for entity_name, language in entry["entities"]:
              if entity_name in SPECIAL_TOKENS:
                  new_mapping[entity_name] = entity_id
                  break
              new_entity_name = f"{language}:{entity_name}"
              new_mapping[new_entity_name] = entity_id
      return new_mapping
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/mluke/convert_mluke_original_pytorch_checkpoint_to_pytorch.py
imports: import argparse
  import json
  import os
  from collections import OrderedDict
  import torch
  from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
  from transformers.tokenization_utils_base import AddedToken
label_desc: Use image node_id 2 for calling a global function with example usage: load_original_entity_vocab(entity_vocab_path) and returns: new_mapping