class_docstr: Runs basic tokenization (punctuation splitting, lower casing, etc.).
class_docstr_tok: ["Runs","basic","tokenization","(","punctuation","splitting",",","lower","casing",",","etc",".",")","."]
docstr: Basic Tokenization of a piece of text.
      Split on 'white spaces' only, for sub-word tokenization, see WordPieceTokenizer.
  Args:
      **never_split**: (`optional`) list of str
          List of token not to split.
docstr_tok: ["Basic","Tokenization","of","a","piece","of","text",".","Split","on","'white","spaces","'","only",",","for","sub-word","tokenization",",","see","WordPieceTokenizer",".","Args",":","*","*","never_split","*","*",":","(","`","optional","`",")","list","of","str","List","of","token","not","to","split","."]
code: def tokenize(self, text: str, never_split: List[str] = None):
      """Basic Tokenization of a piece of text.
          Split on 'white spaces' only, for sub-word tokenization, see WordPieceTokenizer.
      Args:
          **never_split**: (`optional`) list of str
              List of token not to split.
      """
      never_split = self.never_split + (
          never_split if never_split is not None else []
      )
      text = self._clean_text(text)
      # This was added on November 1st, 2018 for the multilingual and Chinese
      # models. This is also applied to the English models now, but it doesn't
      # matter since the English models were not trained on any Chinese data
      # and generally don't have any Chinese data in them (there are Chinese
      # characters in the vocabulary because Wikipedia does have some Chinese
      # words in the English Wikipedia.).
      if self.tokenize_chinese_chars:
          text = self._tokenize_chinese_chars(text)
      orig_tokens = whitespace_tokenize(text)
      split_tokens = []
      for token in orig_tokens:
          if self.do_lower_case and token not in never_split:
              token = token.lower()
              token = self._run_strip_accents(token)
          split_tokens.extend(
              self._run_split_on_punc(token, never_split)
          )
  
      output_tokens = whitespace_tokenize(" ".join(split_tokens))
      return output_tokens
  
imports: import collections
  import os
  import pickle
  import unicodedata
  from typing import Dict, List, Optional, Union, Tuple
  from paddle.utils import try_import
  from paddlehub.text.utils import load_vocab, is_whitespace, is_control, is_punctuation, whitespace_tokenize, is_chinese_char
label_desc: Use image node_id 2 for calling the BasicTokenizer obj's underlying member method code with example usage: obj.tokenize(text, never_split) and returns: output_tokens