class_docstr: Re-implementation of Tercom Tokenizer in Python 3.
  
  See src/ter/core/Normalizer.java in https://github.com/jhclark/tercom Note that Python doesn't support named Unicode
  blocks so the mapping for relevant blocks was taken from here: https://unicode-table.com/en/blocks/
  
  This implementation follows the implementation from
  https://github.com/mjpost/sacrebleu/blob/master/sacrebleu/tokenizers/tokenizer_ter.py.
docstr: Remove punctuation from an input sentence string.
docstr_tok: ["Remove","punctuation","from","an","input","sentence","string","."]
code: def _remove_punct(sentence: str) -> str:
      """Remove punctuation from an input sentence string."""
      return re.sub(r"[\.,\?:;!\"\(\)]", "", sentence)
  
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 5 for calling the _TercomTokenizer obj's underlying member method code with example usage: obj._remove_punct(sentence) and returns: re