code: def test_overwrite_token():
      nlp = English()
      nlp.add_pipe("ner")
      nlp.initialize()
      # The untrained NER will predict O for each token
      doc = nlp("I live in New York")
      assert [token.ent_iob_ for token in doc] == [
          "O",
          "O",
          "O",
          "O",
          "O",
      ]
      assert [token.ent_type_ for token in doc] == ["", "", "", "", ""]
      # Check that a new ner can overwrite O
      config = {}
      ner2 = nlp.create_pipe("ner", config=config)
      ner2.moves.add_action(5, "")
      ner2.add_label("GPE")
      state = ner2.moves.init_batch([doc])[0]
      assert ner2.moves.is_valid(state, "B-GPE")
      assert ner2.moves.is_valid(state, "U-GPE")
      ner2.moves.apply_transition(state, "B-GPE")
      assert ner2.moves.is_valid(state, "I-GPE")
      assert ner2.moves.is_valid(state, "L-GPE")
  
imports: import logging
  import random
  import pytest
  from numpy.testing import assert_equal
  from spacy import registry, util
  from spacy.attrs import ENT_IOB
  from spacy.lang.en import English
  from spacy.lang.it import Italian
  from spacy.language import Language
  from spacy.lookups import Lookups
  from spacy.pipeline import EntityRecognizer
  from spacy.pipeline._parser_internals.ner import BiluoPushDown
  from spacy.pipeline.ner import DEFAULT_NER_MODEL
  from spacy.tokens import Doc, Span
  from spacy.training import Example, iob_to_biluo, split_bilu_label
  from spacy.vocab import Vocab
  from ..util import make_tempdir
label_desc: Use image node_id 25 for calling a global function with example usage: test_overwrite_token() without return types