code: def test_simple_train():
      nlp = Language()
      textcat = nlp.add_pipe("textcat")
      textcat.add_label("answer")
      nlp.initialize()
      for i in range(5):
          for text, answer in [
              ("aaaa", 1.0),
              ("bbbb", 0),
              ("aa", 1.0),
              ("bbbbbbbbb", 0.0),
              ("aaaaaa", 1),
          ]:
              nlp.update((text, {"cats": {"answer": answer}}))
      doc = nlp("aaa")
      assert "answer" in doc.cats
      assert doc.cats["answer"] >= 0.5
  
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 9 for calling a global function with example usage: test_simple_train() without return types