code: def test_positive_class():
      nlp = English()
      textcat = nlp.add_pipe("textcat")
      get_examples = make_get_examples_single_label(nlp)
      textcat.initialize(
          get_examples, labels=["POS", "NEG"], positive_label="POS"
      )
      assert textcat.labels == ("POS", "NEG")
      assert textcat.cfg["positive_label"] == "POS"
  
      textcat_multilabel = nlp.add_pipe("textcat_multilabel")
      get_examples = make_get_examples_multi_label(nlp)
      with pytest.raises(TypeError):
          textcat_multilabel.initialize(
              get_examples, labels=["POS", "NEG"], positive_label="POS"
          )
      textcat_multilabel.initialize(
          get_examples, labels=["FICTION", "DRAMA"]
      )
      assert textcat_multilabel.labels == ("FICTION", "DRAMA")
      assert "positive_label" not in textcat_multilabel.cfg
  
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 23 for calling a global function with example usage: test_positive_class() without return types