code: def test_doc_retokenize_spans_merge_tokens_default_attrs(en_vocab):
      words = ["The", "players", "start", "."]
      lemmas = [t.lower() for t in words]
      heads = [1, 2, 2, 2]
      deps = ["dep"] * len(heads)
      tags = ["DT", "NN", "VBZ", "."]
      pos = ["DET", "NOUN", "VERB", "PUNCT"]
      doc = Doc(
          en_vocab,
          words=words,
          tags=tags,
          pos=pos,
          heads=heads,
          deps=deps,
          lemmas=lemmas,
      )
      assert len(doc) == 4
      assert doc[0].text == "The"
      assert doc[0].tag_ == "DT"
      assert doc[0].pos_ == "DET"
      assert doc[0].lemma_ == "the"
      with doc.retokenize() as retokenizer:
          retokenizer.merge(doc[0:2])
      assert len(doc) == 3
      assert doc[0].text == "The players"
      assert doc[0].tag_ == "NN"
      assert doc[0].pos_ == "NOUN"
      assert doc[0].lemma_ == "the players"
      doc = Doc(
          en_vocab,
          words=words,
          tags=tags,
          pos=pos,
          heads=heads,
          deps=deps,
          lemmas=lemmas,
      )
      assert len(doc) == 4
      assert doc[0].text == "The"
      assert doc[0].tag_ == "DT"
      assert doc[0].pos_ == "DET"
      assert doc[0].lemma_ == "the"
      with doc.retokenize() as retokenizer:
          retokenizer.merge(doc[0:2])
          retokenizer.merge(doc[2:4])
      assert len(doc) == 2
      assert doc[0].text == "The players"
      assert doc[0].tag_ == "NN"
      assert doc[0].pos_ == "NOUN"
      assert doc[0].lemma_ == "the players"
      assert doc[1].text == "start ."
      assert doc[1].tag_ == "VBZ"
      assert doc[1].pos_ == "VERB"
      assert doc[1].lemma_ == "start ."
  
imports: import pytest
  from spacy.attrs import LEMMA
  from spacy.tokens import Doc, Token
  from spacy.vocab import Vocab
label_desc: Use image node_id 8 for calling a global function with example usage: test_doc_retokenize_spans_merge_tokens_default_attrs(en_vocab) without return types