class_docstr: Spacy doesn't assume that text is tokenised. Sometimes this
  is annoying, like when you have gold data which is pre-tokenised,
  but Spacy's tokenisation doesn't match the gold. This can be used
  as follows:
  nlp = spacy.load("en_core_web_md")
  # hack to replace tokenizer with a whitespace tokenizer
  nlp.tokenizer = WhitespaceTokenizer(nlp.vocab)
  ... use nlp("here is some text") as normal.
code: def __call__(self, text):
      words = text.split(" ")
      # All tokens 'own' a subsequent space character in
      # this tokenizer. This is a technicality and probably
      # not that interesting.
      spaces = [True] * len(words)
      return Doc(self.vocab, words=words, spaces=spaces)
  
file_path: scispacy/scispacy/util.py
imports: import spacy
  from spacy.language import Language
  from spacy.tokens import Doc
  from scispacy.custom_sentence_segmenter import pysbd_sentencizer
  from scispacy.custom_tokenizer import combined_rule_tokenizer
label_desc: Use image node_id 2 for calling the WhitespaceTokenizer obj's underlying member method code with example usage: obj.__call__(text) and returns: Doc