code: def push_to_hf(
      repo_name: str,
      serialization_dir: Optional[Union[str, PathLike]] = None,
      archive_path: Optional[Union[str, PathLike]] = None,
      organization: Optional[str] = None,
      commit_message: str = "Update repository",
      local_repo_path: Union[str, PathLike] = "hub",
      use_auth_token: Union[bool, str] = True,
  ) -> str:
      """Pushes model and related files to the Hugging Face Hub ([hf.co](https://hf.co/))
  
      # Parameters
  
      repo_name: `str`
          Name of the repository in the Hugging Face Hub.
  
      serialization_dir : `Union[str, PathLike]`, optional (default = `None`)
          Full path to a directory with the serialized model.
  
      archive_path : `Union[str, PathLike]`, optional (default = `None`)
          Full path to the zipped model (e.g. model/model.tar.gz). Use `serialization_dir` if possible.
  
      organization : `Optional[str]`, optional (default = `None`)
          Name of organization to which the model should be uploaded.
  
      commit_message: `str` (default=`Update repository`)
          Commit message to use for the push.
  
      local_repo_path : `Union[str, Path]`, optional (default=`hub`)
          Local directory where the repository will be saved.
  
      use_auth_token (``str`` or ``bool``, `optional`, defaults ``True``):
          huggingface_token can be extract from ``HfApi().login(username, password)`` and is used to authenticate
          against the Hugging Face Hub (useful from Google Colab for instance). It's automatically retrieved
          if you've done `huggingface-cli login` before.
      """
  
      if serialization_dir is not None:
          working_dir = Path(serialization_dir)
          if archive_path is not None:
              raise ValueError(
                  "serialization_dir and archive_path are mutually exclusive, please just use one."
              )
          if not working_dir.exists() or not working_dir.is_dir():
              raise ValueError(
                  f"Can't find path: {serialization_dir}, please point"
                  "to a directory with the serialized model."
              )
      elif archive_path is not None:
          working_dir = Path(archive_path)
          if (
              not working_dir.exists()
              or not zipfile.is_zipfile(working_dir)
              and not tarfile.is_tarfile(working_dir)
          ):
              raise ValueError(
                  f"Can't find path: {archive_path}, please point to a .tar.gz archive"
                  "or to a directory with the serialized model."
              )
          else:
              logging.info(
                  "Using the archive_path is discouraged. Using the serialization_dir"
                  "will also upload metrics and TensorBoard traces to the Hugging Face Hub."
              )
      else:
          raise ValueError(
              "please specify either serialization_dir or archive_path"
          )
  
      info_msg = f"Preparing repository '{use_auth_token}'"
      if isinstance(use_auth_token, str):
          huggingface_token = use_auth_token
      elif use_auth_token:
          huggingface_token = HfFolder.get_token()
  
      # Create the repo (or clone its content if it's nonempty)
      api = HfApi()
      repo_url = api.create_repo(
          name=repo_name,
          token=huggingface_token,
          organization=organization,
          private=False,
          exist_ok=True,
      )
  
      repo_local_path = Path(local_repo_path) / repo_name
      repo = Repository(
          repo_local_path,
          clone_from=repo_url,
          use_auth_token=use_auth_token,
      )
      repo.git_pull(rebase=True)
  
      # Model file should be tracked with Git LFS
      repo.lfs_track(["*.th"])
      info_msg = f"Preparing repository '{repo_name}'"
      if organization is not None:
          info_msg += f" ({organization})"
      logging.info(info_msg)
  
      # Extract information from either serializable directory or a
      # .tar.gz file
      if serialization_dir is not None:
          for filename in working_dir.iterdir():
              _copy_allowed_file(Path(filename), repo_local_path)
      else:
          with tempfile.TemporaryDirectory() as temp_dir:
              extracted_dir = Path(
                  cached_path(
                      working_dir, temp_dir, extract_archive=True
                  )
              )
              for filename in extracted_dir.iterdir():
                  _copy_allowed_file(Path(filename), repo_local_path)
  
      _create_model_card(repo_local_path)
  
      logging.info(f"Pushing repo {repo_name} to the Hugging Face Hub")
      repo.push_to_hub(commit_message=commit_message)
  
      logging.info(f"View your model in {repo_url}")
      return repo_url
  
file_path: allennlp/allennlp/common/push_to_hf.py
imports: import logging
  import shutil
  import tarfile
  import tempfile
  import zipfile
  from os import PathLike
  from pathlib import Path
  from typing import Optional, Union
  from huggingface_hub import HfApi, HfFolder, Repository
  from allennlp.common.file_utils import cached_path
label_desc: Use image node_id 3 for calling a global function with example usage: push_to_hf(repo_name, serialization_dir, archive_path, organization, commit_message, local_repo_path, use_auth_token) and returns: repo_url