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 set_vocab(self, vocab: Iterable[str]):
      self.vocab = vocab
      self.word2idx = collections.OrderedDict(
          [(word, idx) for idx, word in enumerate(vocab)]
      )
  
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 3 for calling the WhitespaceTokenizer obj's underlying member method code with example usage: obj.set_vocab(vocab) without return types