class_docstr: Does really simple tokenization.  NLTK was too slow, so we wrote our own simple tokenizer
  instead.  This just does an initial split(), followed by some heuristic filtering of each
  whitespace-delimited token, separating contractions and punctuation.  We assume lower-cased,
  reasonably well-formed English sentences as input.
class_docstr_tok: ["Does","really","simple","tokenization",".","NLTK","was","too","slow",",","so","we","wrote","our","own","simple","tokenizer","instead",".","This","just","does","an","initial","split","(",")",",","followed","by","some","heuristic","filtering","of","each","whitespace-delimited","token",",","separating","contractions","and","punctuation",".","We","assume","lower-cased",",","reasonably","well-formed","English","sentences","as","input","."]
code: def _can_split(self, token):
      return token and token.lower() not in self.special_cases
  
imports: from __future__ import absolute_import
  import re
  import spacy
  from allennlp.common import Registrable
  from allennlp.common.util import get_spacy_model
  from allennlp.data.tokenizers.token import Token
label_desc: Use image node_id 3 for calling the SimpleWordSplitter obj's underlying member method code with example usage: obj._can_split(token) and returns: unknown