code: def test_initialize_examples(name, get_examples, train_data):
      nlp = Language()
      textcat = nlp.add_pipe(name)
      for text, annotations in train_data:
          for label, value in annotations.get("cats").items():
              textcat.add_label(label)
      # you shouldn't really call this more than once, but for testing it should be fine
      nlp.initialize()
      nlp.initialize(get_examples=get_examples(nlp))
      with pytest.raises(TypeError):
          nlp.initialize(get_examples=lambda: None)
      with pytest.raises(TypeError):
          nlp.initialize(get_examples=get_examples())
  
imports: import random
  import numpy.random
  import pytest
  from numpy.testing import assert_almost_equal
  from thinc.api import Config, compounding, fix_random_seed, get_current_ops
  from wasabi import msg
  import spacy
  from spacy import util
  from spacy.cli.evaluate import print_prf_per_type, print_textcats_auc_per_cat
  from spacy.lang.en import English
  from spacy.language import Language
  from spacy.pipeline import TextCategorizer
  from spacy.pipeline.textcat import single_label_bow_config, single_label_cnn_config, single_label_default_config
  from spacy.pipeline.textcat_multilabel import multi_label_bow_config, multi_label_cnn_config, multi_label_default_config
  from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
  from spacy.scorer import Scorer
  from spacy.tokens import Doc, DocBin
  from spacy.training import Example
  from spacy.training.initialize import init_nlp
  from ..util import make_tempdir
label_desc: Use image node_id 19 for calling a global function with example usage: test_initialize_examples(name, get_examples, train_data) without return types