code: def _generate_model_card(self, repo_id):
      return f"""---
  tags:
  - flair
  - token-classification
  - sequence-tagger-model
  ---
  
  ### Demo: How to use in Flair
  
  Requires:
  - **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
  
  ```python
  from flair.data import Sentence
  from flair.models import SequenceTagger
  
  # load tagger
  tagger = SequenceTagger.load("{repo_id}")
  
  # make example sentence
  sentence = Sentence("On September 1st George won 1 dollar while watching Game of Thrones.")
  
  # predict NER tags
  tagger.predict(sentence)
  
  # print sentence
  print(sentence)
  
  # print predicted NER spans
  print('The following NER tags are found:')
  
  # iterate over entities and print
  for entity in sentence.get_spans('ner'):
      print(entity)
  ```"""
  
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 20 for calling the SequenceTagger obj's underlying member method code with example usage: obj._generate_model_card(repo_id) and returns: str, repo_id, str