docstr: Uploads the Sequence Tagger model to a Hugging Face Hub repository.
  
  Args:
      repo_id: A namespace (user or an organization) and a repo name separated by a `/`.
      token: An authentication token (See https://huggingface.co/settings/token).
      private: Whether the repository is private.
      commit_message: Message to commit while pushing.
  
  Returns: The url of the repository.
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code: def push_to_hub(
      self,
      repo_id: str,
      token: Optional[str] = None,
      private: Optional[bool] = None,
      commit_message: str = "Add new SequenceTagger model.",
  ):
      """Uploads the Sequence Tagger model to a Hugging Face Hub repository.
  
      Args:
          repo_id: A namespace (user or an organization) and a repo name separated by a `/`.
          token: An authentication token (See https://huggingface.co/settings/token).
          private: Whether the repository is private.
          commit_message: Message to commit while pushing.
  
      Returns: The url of the repository.
      """
      # Lazy import
      from huggingface_hub import create_repo, model_info, upload_folder
  
      repo_url = create_repo(
          repo_id=repo_id,
          token=token,
          private=private,
          exist_ok=True,
      )
  
      with tempfile.TemporaryDirectory() as tmp_dir:
          tmp_path = Path(tmp_dir)
  
          # Save model weight
          local_model_path = tmp_path / "pytorch_model.bin"
          self.save(local_model_path)
  
          # Determine if model card already exists
          info = model_info(repo_id, use_auth_token=token)
          write_readme = all(
              f.rfilename != "README.md" for f in info.siblings
          )
  
          # Generate and save model card
          if write_readme:
              model_card_content = self._generate_model_card(repo_id)
              readme_path = tmp_path / "README.md"
              with readme_path.open("w", encoding="utf-8") as f:
                  f.write(model_card_content)
  
          # Upload files
          upload_folder(
              repo_id=repo_id,
              folder_path=tmp_path,
              path_in_repo="",
              token=token,
              commit_message=commit_message,
          )
          return repo_url
  
imports: import logging
  import tempfile
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Tuple, Union, cast
  from urllib.error import HTTPError
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  from tqdm import tqdm
  import flair.nn
  from flair.data import Dictionary, Label, Sentence, Span, get_spans_from_bio
  from flair.datasets import DataLoader, FlairDatapointDataset
  from flair.embeddings import TokenEmbeddings
  from flair.file_utils import cached_path, unzip_file
  from flair.models.sequence_tagger_utils.crf import CRF
  from flair.models.sequence_tagger_utils.viterbi import ViterbiDecoder, ViterbiLoss
  from flair.training_utils import store_embeddings
label_desc: Use image node_id 21 for calling the SequenceTagger obj's underlying member method code with example usage: obj.push_to_hub(repo_id, token, private, commit_message) and returns: repo_url