code: def test_overfitting_IO():
      # Simple test to try and quickly overfit the senter - ensuring the ML models work correctly
      nlp = English()
      train_examples = []
      for t in TRAIN_DATA:
          train_examples.append(
              Example.from_dict(nlp.make_doc(t[0]), t[1])
          )
      # add some cases where SENT_START == -1
      train_examples[0].reference[10].is_sent_start = False
      train_examples[1].reference[1].is_sent_start = False
      train_examples[1].reference[11].is_sent_start = False
  
      nlp.add_pipe("senter")
      optimizer = nlp.initialize()
  
      for i in range(200):
          losses = {}
          nlp.update(train_examples, sgd=optimizer, losses=losses)
      assert losses["senter"] < 0.001
  
      # test the trained model
      test_text = TRAIN_DATA[0][0]
      doc = nlp(test_text)
      gold_sent_starts = [0] * 14
      gold_sent_starts[0] = 1
      gold_sent_starts[5] = 1
      gold_sent_starts[9] = 1
      assert [int(t.is_sent_start) for t in doc] == gold_sent_starts
  
      # Also test the results are still the same after IO
      with make_tempdir() as tmp_dir:
          nlp.to_disk(tmp_dir)
          nlp2 = util.load_model_from_path(tmp_dir)
          doc2 = nlp2(test_text)
          assert [
              int(t.is_sent_start) for t in doc2
          ] == gold_sent_starts
  
      # Make sure that running pipe twice, or comparing to call, always amounts to the same predictions
      texts = [
          "Just a sentence.",
          "Then one more sentence about London.",
          "Here is another one.",
          "I like London.",
      ]
      batch_deps_1 = [
          doc.to_array([SENT_START]) for doc in nlp.pipe(texts)
      ]
      batch_deps_2 = [
          doc.to_array([SENT_START]) for doc in nlp.pipe(texts)
      ]
      no_batch_deps = [
          doc.to_array([SENT_START])
          for doc in [nlp(text) for text in texts]
      ]
      assert_equal(batch_deps_1, batch_deps_2)
      assert_equal(batch_deps_1, no_batch_deps)
  
      # test internal pipe labels vs. Language.pipe_labels with hidden labels
      assert nlp.get_pipe("senter").labels == ("I", "S")
      assert "senter" not in nlp.pipe_labels
  
file_path: spaCy/spacy/tests/pipeline/test_senter.py
imports: import pytest
  from numpy.testing import assert_equal
  from spacy import util
  from spacy.attrs import SENT_START
  from spacy.lang.en import English
  from spacy.language import Language
  from spacy.tests.util import make_tempdir
  from spacy.training import Example
label_desc: Use image node_id 3 for calling a global function with example usage: test_overfitting_IO() without return types