code: def _preprocess_sentence(
      sentence: str, tokenizer: _TercomTokenizer
  ) -> str:
      """Given a sentence, apply tokenization.
  
      Args:
          sentence: The input sentence string.
          tokenizer: An instance of ``_TercomTokenizer`` handling a sentence tokenization.
  
      Return:
          The pre-processed output sentence string.
  
      """
      return tokenizer(sentence.rstrip())
  
imports: import re
  from functools import lru_cache
  from typing import Dict, Iterator, List, Optional, Sequence, Tuple, Type, Union
  from torch import Tensor, tensor
  from torchmetrics.functional.text.helper import _flip_trace, _LevenshteinEditDistance, _trace_to_alignment, _validate_inputs
label_desc: Use image node_id 1 for calling a global function with example usage: _preprocess_sentence(sentence, tokenizer) and returns: tokenizer