code: def test_doc_retokenize_split_lemmas(en_vocab):
      # If lemmas are not set, leave unset
      words = ["LosAngeles", "start", "."]
      heads = [1, 2, 2]
      deps = ["dep"] * len(heads)
      doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
      with doc.retokenize() as retokenizer:
          retokenizer.split(
              doc[0],
              ["Los", "Angeles"],
              [(doc[0], 1), doc[1]],
          )
      assert doc[0].lemma_ == ""
      assert doc[1].lemma_ == ""
  
      # If lemmas are set, use split orth as default lemma
      words = ["LosAngeles", "start", "."]
      heads = [1, 2, 2]
      deps = ["dep"] * len(heads)
      doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
      for t in doc:
          t.lemma_ = "a"
      with doc.retokenize() as retokenizer:
          retokenizer.split(
              doc[0],
              ["Los", "Angeles"],
              [(doc[0], 1), doc[1]],
          )
      assert doc[0].lemma_ == "Los"
      assert doc[1].lemma_ == "Angeles"
  
file_path: spaCy/spacy/tests/doc/test_retokenize_split.py
imports: import numpy
  import pytest
  from spacy.tokens import Doc, Token
  from spacy.vocab import Vocab
label_desc: Use image node_id 3 for calling a global function with example usage: test_doc_retokenize_split_lemmas(en_vocab) without return types