code: def whitespace_nlp_with_fake_chunks(
      doc, entity_type=None, tag_type=None
  ):
      toks = _regex_parse_sentence(doc, entity_type, tag_type)
      words = [t for t in toks if t.pos_ == "WORD"]
      if len(words) < 5:
          return Doc([toks])
      else:
          return Doc(
              [toks], noun_chunks=[Span(words[:2]), Span(words[1:3])]
          )
  
imports: import re
  from collections import Counter
  from unittest import TestCase
  from scattertext import whitespace_nlp
  from scattertext.WhitespaceNLP import Tok, Doc, _regex_parse_sentence, whitespace_nlp_with_sentences
  from scattertext.features.FeatsFromSpacyDoc import FeatsFromSpacyDoc
  from scattertext.features.FeatsFromSpacyDocOnlyNounChunks import FeatsFromSpacyDocOnlyNounChunks
label_desc: Use image node_id 2 for calling a global function with example usage: whitespace_nlp_with_fake_chunks(doc, entity_type, tag_type) and returns: Doc, Doc