code: def test_incomplete_data(top_k):
      # Test that the lemmatizer works with incomplete information
      nlp = English()
      lemmatizer = nlp.add_pipe(
          "trainable_lemmatizer", config={"top_k": top_k}
      )
      lemmatizer.min_tree_freq = 1
      train_examples = []
      for t in PARTIAL_DATA:
          train_examples.append(
              Example.from_dict(nlp.make_doc(t[0]), t[1])
          )
      optimizer = nlp.initialize(get_examples=lambda: train_examples)
      for i in range(50):
          losses = {}
          nlp.update(train_examples, sgd=optimizer, losses=losses)
      assert losses["trainable_lemmatizer"] < 0.00001
  
      # test the trained model
      test_text = "She likes blue eggs"
      doc = nlp(test_text)
      assert doc[1].lemma_ == "like"
      assert doc[2].lemma_ == "blue"
  
      # Check that incomplete annotations are ignored.
      scores, _ = lemmatizer.model(
          [eg.predicted for eg in train_examples], is_train=True
      )
      _, dX = lemmatizer.get_loss(train_examples, scores)
      xp = lemmatizer.model.ops.xp
  
      # Missing annotations.
      assert xp.count_nonzero(dX[0][0]) == 0
      assert xp.count_nonzero(dX[0][3]) == 0
      assert xp.count_nonzero(dX[1][0]) == 0
      assert xp.count_nonzero(dX[1][3]) == 0
  
      # Misaligned annotations.
      assert xp.count_nonzero(dX[1][1]) == 0
  
imports: import pickle
  import hypothesis.strategies
  import pytest
  from hypothesis import given
  from spacy import util
  from spacy.lang.en import English
  from spacy.language import Language
  from spacy.pipeline._edit_tree_internals.edit_trees import EditTrees
  from spacy.strings import StringStore
  from spacy.training import Example
  from spacy.util import make_tempdir
label_desc: Use image node_id 4 for calling a global function with example usage: test_incomplete_data(top_k) without return types