code: def test_doc_retokenizer_split_lex_attrs(en_vocab):
      """Test that retokenization also sets attributes on the lexeme if they're
      lexical attributes. For example, if a user sets IS_STOP, it should mean that
      "all tokens with that lexeme" are marked as a stop word, so the ambiguity
      here is acceptable. Also see #2390.
      """
      assert not Doc(en_vocab, words=["Los"])[0].is_stop
      assert not Doc(en_vocab, words=["Angeles"])[0].is_stop
      doc = Doc(en_vocab, words=["LosAngeles", "start"])
      assert not doc[0].is_stop
      with doc.retokenize() as retokenizer:
          attrs = {"is_stop": [True, False]}
          heads = [(doc[0], 1), doc[1]]
          retokenizer.split(
              doc[0], ["Los", "Angeles"], heads, attrs=attrs
          )
      assert doc[0].is_stop
      assert not doc[1].is_stop
  
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 11 for calling a global function with example usage: test_doc_retokenizer_split_lex_attrs(en_vocab) without return types