code: def test_multilabel_classification() -> None:
      from sklearn.datasets import make_multilabel_classification
  
      X, y = make_multilabel_classification(
          n_samples=32, n_classes=5, n_labels=3, random_state=0
      )
      clf = xgb.XGBClassifier(tree_method="hist")
      clf.fit(X, y)
      booster = clf.get_booster()
      learner = json.loads(booster.save_config())["learner"]
      assert int(learner["learner_model_param"]["num_target"]) == 5
  
      np.testing.assert_allclose(clf.predict(X), y)
      predt = (clf.predict_proba(X) > 0.5).astype(np.int64)
      np.testing.assert_allclose(clf.predict(X), predt)
      assert predt.dtype == np.int64
  
      y = y.tolist()
      clf.fit(X, y)
      np.testing.assert_allclose(clf.predict(X), predt)
  
imports: import json
  import os
  import pickle
  import random
  import tempfile
  import warnings
  from typing import Callable, Optional
  import numpy
  import pytest
  from sklearn.utils.estimator_checks import parametrize_with_checks
  import xgboost
  from xgboost import testing
  from xgboost.testing.ranking import run_ranking_categorical, run_ranking_qid_df
  from xgboost.testing.shared import get_feature_weights, validate_data_initialization
  from xgboost.testing.updater import get_basescore
label_desc: Use image node_id 46 for calling a global function with example usage: test_multilabel_classification() without return types