code: def export_model(
      hf_pretrained_model_name_or_path: str,
      output_base_path: str,
  ):
      """Retrieve model and tokenizer from Transformers and save all necessary data
      Things saved:
      - Model weights
      - Model config JSON (corresponding to corresponding Transformers model Config object)
      - Tokenizer data
      - JSON file pointing to paths for the above
      Args:
          hf_pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
                          Can be either:
  
                              - A string, the `model id` of a pretrained model configuration
                                hosted inside a model repo on okhuggingface.co.
                                Valid model ids can be located at the root-level, like
                                ``bert-base-uncased``, or namespaced under a user
                                or organization name, like ``dbmdz/bert-base-german-cased``.
                              - A path to a `directory` containing a configuration file saved using
                                the :meth:`~transformers.PretrainedConfig.save_pretrained` method,
                                or the
                                :meth:`~transformers.PreTrainedModel.save_pretrained` method,
                                e.g., ``./my_model_directory/``.
                              - A path or url to a saved configuration JSON `file`, e.g.,
                                ``./my_model_directory/configuration.json``.
          output_base_path: Base path to save output to
      """
      model = AutoModelForPreTraining.from_pretrained(
          hf_pretrained_model_name_or_path
      )
  
      model_fol_path = os.path.join(output_base_path, "model")
      model_path = os.path.join(model_fol_path, "model.p")
      model_config_path = os.path.join(model_fol_path, "config.json")
      tokenizer_fol_path = os.path.join(output_base_path, "tokenizer")
  
      os.makedirs(tokenizer_fol_path, exist_ok=True)
      os.makedirs(model_fol_path, exist_ok=True)
  
      torch.save(model.state_dict(), model_path)
      py_io.write_json(model.config.to_dict(), model_config_path)
      tokenizer = AutoTokenizer.from_pretrained(
          hf_pretrained_model_name_or_path, use_fast=False
      )
      tokenizer.save_pretrained(tokenizer_fol_path)
      config = {
          "hf_pretrained_model_name_or_path": hf_pretrained_model_name_or_path,
          "model_path": model_path,
          "model_config_path": model_config_path,
      }
      py_io.write_json(
          config, os.path.join(output_base_path, "config.json")
      )
  
file_path: jiant/jiant/proj/main/export_model.py
imports: import os
  import torch
  from transformers import AutoModelForPreTraining, AutoTokenizer
  import jiant.utils.python.io
  import jiant.utils.zconf
label_desc: Use image node_id 1 for calling a global function with example usage: export_model(hf_pretrained_model_name_or_path, output_base_path) without return types