class_docstr: Simple and fast white-space tokenizer. Splits sentence based on white spaces.
  Punctuation are stripped from tokens.
class_docstr_tok: ["Simple","and","fast","white-space","tokenizer",".","Splits","sentence","based","on","white","spaces",".","Punctuation","are","stripped","from","tokens","."]
code: def tokenize(self, text: str) -> List[int]:
      if self.do_lower_case:
          text = text.lower()
  
      tokens = text.split()
  
      tokens_filtered = []
      for token in tokens:
          if token in self.stop_words:
              continue
          elif token in self.word2idx:
              tokens_filtered.append(self.word2idx[token])
              continue
  
          token = token.strip(string.punctuation)
          if token in self.stop_words:
              continue
          elif len(token) > 0 and token in self.word2idx:
              tokens_filtered.append(self.word2idx[token])
              continue
  
          token = token.lower()
          if token in self.stop_words:
              continue
          elif token in self.word2idx:
              tokens_filtered.append(self.word2idx[token])
              continue
  
      return tokens_filtered
  
file_path: sentence-transformers/sentence_transformers/models/tokenizer/WhitespaceTokenizer.py
imports: from typing import List, Iterable
  import collections
  import string
  import os
  import json
  from .WordTokenizer import WordTokenizer, ENGLISH_STOP_WORDS
label_desc: Use image node_id 4 for calling the WhitespaceTokenizer obj's underlying member method code with example usage: obj.tokenize(text) and returns: tokens_filtered