docstr: This tokenizers splits a raw_token into its constituent words by splitting
  the raw_token on space. This function handle multiple spaces between
  words too.
  Note:
  torch scripting doesn't support try-except and since re.finditer uses
  try in its implemetation regex based tokenization is not supported.
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code: def tokenize(self, raw_token: str) -> List[Tuple[str, int, int]]:
      """
      This tokenizers splits a raw_token into its constituent words by splitting
      the raw_token on space. This function handle multiple spaces between
      words too.
      Note:
      torch scripting doesn't support try-except and since re.finditer uses
      try in its implemetation regex based tokenization is not supported.
      """
      tokenize_input = (
          raw_token.lower() if self.lowercase else raw_token
      )
      tokens = tokenize_input.split()
      torchify_tokens = torch.jit.annotate(
          List[Tuple[str, int, int]], []
      )
      start, end = 0, 0
      for token in tokens:
          start = tokenize_input.find(token, end, -1)
          end = start + len(token)
          torchify_tokens.append((token.strip(), start, end))
          start = end + 1
      return torchify_tokens
  
file_path: pytext/pytext/torchscript/tokenizer/tokenizer.py
imports: from typing import List, Tuple
  import torch
  from .bpe import ScriptBPE
label_desc: Use image node_id 2 for calling the ScriptWordTokenizer obj's underlying member method code with example usage: obj.tokenize(raw_token) and returns: torchify_tokens