class_docstr: Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
  
  Args:
      do_lower_case (`bool`, *optional*, defaults to `True`):
          Whether or not to lowercase the input when tokenizing.
      never_split (`Iterable`, *optional*):
          Collection of tokens which will never be split during tokenization. Only has an effect when
          `do_basic_tokenize=True`
      tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
          Whether or not to tokenize Chinese characters.
  
          This should likely be deactivated for Japanese (see this
          [issue](https://github.com/huggingface/transformers/issues/328)).
      strip_accents (`bool`, *optional*):
          Whether or not to strip all accents. If this option is not specified, then it will be determined by the
          value for `lowercase` (as in the original BERT).
      do_split_on_punc (`bool`, *optional*, defaults to `True`):
          In some instances we want to skip the basic punctuation splitting so that later tokenization can capture
          the full context of the words, such as contractions.
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docstr: Strips accents from a piece of text.
docstr_tok: ["Strips","accents","from","a","piece","of","text","."]
code: def _run_strip_accents(self, text):
      """Strips accents from a piece of text."""
      text = unicodedata.normalize("NFD", text)
      output = []
      for char in text:
          cat = unicodedata.category(char)
          if cat == "Mn":
              continue
          output.append(char)
      return "".join(output)
  
imports: import collections
  import os
  import unicodedata
  from typing import List, Optional, Tuple
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
  from ...utils import logging
label_desc: Use image node_id 3 for calling the BasicTokenizer obj's underlying member method code with example usage: obj._run_strip_accents(text) and returns: str