class_docstr: A ``WordSplitter`` that assumes you've already done your own tokenization somehow and have
  separated the tokens by spaces.  We just split the input string on whitespace and return the
  resulting list.  We use a somewhat odd name here to avoid coming too close to the more
  commonly used ``SpacyWordSplitter``.
  
  Note that we use ``sentence.split()``, which means that the amount of whitespace between the
  tokens does not matter.  This will never result in spaces being included as tokens.
class_docstr_tok: ["A","``","WordSplitter","``","that","assumes","you","'ve","already","done","your","own","tokenization","somehow","and","have","separated","the","tokens","by","spaces",".","We","just","split","the","input","string","on","whitespace","and","return","the","resulting","list",".","We","use","a","somewhat","odd","name","here","to","avoid","coming","too","close","to","the","more","commonly","used","``","SpacyWordSplitter","``",".","Note","that","we","use","``","sentence.split","(",")","``",",","which","means","that","the","amount","of","whitespace","between","the","tokens","does","not","matter",".","This","will","never","result","in","spaces","being","included","as","tokens","."]
code: def split_words(self, sentence):
      return [Token(t) for t in sentence.split()]
  
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 1 for calling the JustSpacesWordSplitter obj's underlying member method code with example usage: obj.split_words(sentence) and returns: unknown