code: def clf_predict_proba(classif, covmats, labels):
      n_matrices = len(labels)
      n_classes = len(np.unique(labels))
      clf = classif()
      if hasattr(clf, "probability"):
          clf.set_params(**{"probability": True})
      clf.fit(covmats, labels)
      probabilities = clf.predict_proba(covmats)
      assert probabilities.shape == (n_matrices, n_classes)
      assert probabilities.sum(axis=1) == approx(np.ones(n_matrices))
  
imports: import pickle
  import numpy
  from numpy.testing import assert_array_equal
  import pytest
  from pytest import approx
  from scipy.stats import mode
  from sklearn.dummy import DummyClassifier
  from conftest import get_distances, get_means, get_metrics
  from pyriemann.estimation import Covariances
  from pyriemann.utils.kernel import kernel
  from pyriemann.utils.mean import mean_covariance
  from pyriemann.classification import _mode_2d, MDM, FgMDM, KNearestNeighbor, TSclassifier, SVC, MeanField, class_distinctiveness
label_desc: Use image node_id 4 for calling a global function with example usage: clf_predict_proba(classif, covmats, labels) without return types