code: def test_textcat_multi_threshold():
      # Ensure the scorer can be called with a different threshold
      nlp = English()
      nlp.add_pipe("textcat_multilabel")
  
      train_examples = []
      for text, annotations in TRAIN_DATA_SINGLE_LABEL:
          train_examples.append(
              Example.from_dict(nlp.make_doc(text), annotations)
          )
      nlp.initialize(get_examples=lambda: train_examples)
  
      # score the model (it's not actually trained but that doesn't matter)
      scores = nlp.evaluate(train_examples)
      assert 0 <= scores["cats_score"] <= 1
  
      scores = nlp.evaluate(
          train_examples, scorer_cfg={"threshold": 1.0}
      )
      assert scores["cats_f_per_type"]["POSITIVE"]["r"] == 0
  
      scores = nlp.evaluate(train_examples, scorer_cfg={"threshold": 0})
      assert scores["cats_f_per_type"]["POSITIVE"]["r"] == 1.0
  
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 30 for calling a global function with example usage: test_textcat_multi_threshold() without return types