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 save(self, output_path: str):
      with open(
          os.path.join(output_path, "whitespacetokenizer_config.json"),
          "w",
      ) as fOut:
          json.dump(
              {
                  "vocab": list(self.word2idx.keys()),
                  "stop_words": list(self.stop_words),
                  "do_lower_case": self.do_lower_case,
              },
              fOut,
          )
  
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 5 for calling the WhitespaceTokenizer obj's underlying member method code with example usage: obj.save(output_path) without return types