code: def test_multiclass_multioutput_estimator_predict_proba():
      seed = 542
  
      # make test deterministic
      rng = np.random.RandomState(seed)
  
      # random features
      X = rng.normal(size=(5, 5))
  
      # random labels
      y1 = np.array(["b", "a", "a", "b", "a"]).reshape(
          5, 1
      )  # 2 classes
      y2 = np.array(["d", "e", "f", "e", "d"]).reshape(
          5, 1
      )  # 3 classes
  
      Y = np.concatenate([y1, y2], axis=1)
  
      clf = MultiOutputClassifier(
          LogisticRegression(solver="liblinear", random_state=seed)
      )
  
      clf.fit(X, Y)
  
      y_result = clf.predict_proba(X)
      y_actual = [
          np.array(
              [
                  [0.23481764, 0.76518236],
                  [0.67196072, 0.32803928],
                  [0.54681448, 0.45318552],
                  [0.34883923, 0.65116077],
                  [0.73687069, 0.26312931],
              ]
          ),
          np.array(
              [
                  [0.5171785, 0.23878628, 0.24403522],
                  [0.22141451, 0.64102704, 0.13755846],
                  [0.16751315, 0.18256843, 0.64991843],
                  [0.27357372, 0.55201592, 0.17441036],
                  [0.65745193, 0.26062899, 0.08191907],
              ]
          ),
      ]
  
      for i in range(len(y_actual)):
          assert_almost_equal(y_result[i], y_actual[i])
  
imports: import re
  import numpy
  import pytest
  from joblib import cpu_count
  from sklearn import datasets
  from sklearn.base import ClassifierMixin, clone
  from sklearn.datasets import load_linnerud, make_classification, make_multilabel_classification, make_regression
  from sklearn.dummy import DummyClassifier, DummyRegressor
  from sklearn.ensemble import GradientBoostingRegressor, RandomForestClassifier, StackingRegressor
  from sklearn.exceptions import NotFittedError
  from sklearn.impute import SimpleImputer
  from sklearn.linear_model import Lasso, LinearRegression, LogisticRegression, OrthogonalMatchingPursuit, PassiveAggressiveClassifier, Ridge, SGDClassifier, SGDRegressor
  from sklearn.metrics import jaccard_score, mean_squared_error
  from sklearn.model_selection import GridSearchCV, train_test_split
  from sklearn.multiclass import OneVsRestClassifier
  from sklearn.multioutput import ClassifierChain, MultiOutputClassifier, MultiOutputRegressor, RegressorChain
  from sklearn.pipeline import make_pipeline
  from sklearn.svm import LinearSVC
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils import shuffle
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.fixes import BSR_CONTAINERS, COO_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS, DOK_CONTAINERS, LIL_CONTAINERS
label_desc: Use image node_id 15 for calling a global function with example usage: test_multiclass_multioutput_estimator_predict_proba() without return types