code: def convert_xlm_checkpoint_to_pytorch(
      xlm_checkpoint_path, pytorch_dump_folder_path
  ):
      # Load checkpoint
      chkpt = torch.load(xlm_checkpoint_path, map_location="cpu")
  
      state_dict = chkpt["model"]
  
      # We have the base model one level deeper than the original XLM repository
      two_levels_state_dict = {}
      for k, v in state_dict.items():
          if "pred_layer" in k:
              two_levels_state_dict[k] = v
          else:
              two_levels_state_dict["transformer." + k] = v
  
      config = chkpt["params"]
      config = dict(
          (n, v)
          for n, v in config.items()
          if not isinstance(v, (torch.FloatTensor, numpy.ndarray))
      )
  
      vocab = chkpt["dico_word2id"]
      vocab = dict(
          (
              s + "</w>"
              if s.find("@@") == -1 and i > 13
              else s.replace("@@", ""),
              i,
          )
          for s, i in vocab.items()
      )
  
      # Save pytorch-model
      pytorch_weights_dump_path = (
          pytorch_dump_folder_path + "/" + WEIGHTS_NAME
      )
      pytorch_config_dump_path = (
          pytorch_dump_folder_path + "/" + CONFIG_NAME
      )
      pytorch_vocab_dump_path = (
          pytorch_dump_folder_path
          + "/"
          + VOCAB_FILES_NAMES["vocab_file"]
      )
  
      print(f"Save PyTorch model to {pytorch_weights_dump_path}")
      torch.save(two_levels_state_dict, pytorch_weights_dump_path)
  
      print(f"Save configuration file to {pytorch_config_dump_path}")
      with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
          f.write(json.dumps(config, indent=2) + "\n")
  
      print(f"Save vocab file to {pytorch_config_dump_path}")
      with open(pytorch_vocab_dump_path, "w", encoding="utf-8") as f:
          f.write(json.dumps(vocab, indent=2) + "\n")
  
imports: import argparse
  import json
  import numpy
  import torch
  from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
  from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
label_desc: Use image node_id 1 for calling a global function with example usage: convert_xlm_checkpoint_to_pytorch(xlm_checkpoint_path, pytorch_dump_folder_path) without return types