code: def test_predict_proba(mocker, evidence_type, expected_probas):
      def mock_confusion_matrix(y, y_pred):
          return np.array([[80, 20], [10, 90]])
  
      mocker.patch(
          "sklego.meta.subjective_classifier.confusion_matrix",
          side_effect=mock_confusion_matrix,
      )
      mock_inner_estimator = mocker.Mock(RandomForestClassifier)
      mock_inner_estimator.predict_proba.return_value = np.array(
          [[0.8, 0.2], [1, 0], [0.5, 0.5], [0.2, 0.8]]
      )
      mock_inner_estimator.classes_ = np.array([0, 1])
      subjective_model = SubjectiveClassifier(
          mock_inner_estimator, {0: 0.8, 1: 0.2}, evidence=evidence_type
      )
      subjective_model.fit(np.zeros((10, 10)), np.zeros(10))
      posterior_probabilities = subjective_model.predict_proba(
          np.zeros((4, 2))
      )
      assert posterior_probabilities.shape == (4, 2)
      assert np.isclose(
          posterior_probabilities, np.array(expected_probas), atol=0.01
      ).all()
  
file_path: scikit-lego/tests/test_meta/test_subjective_classifier.py
imports: import numpy
  import pytest
  from sklearn.cluster import DBSCAN
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LogisticRegression, Ridge
  from sklego.common import flatten
  from sklego.meta import SubjectiveClassifier
  from tests.conftest import classifier_checks, general_checks, select_tests
label_desc: Use image node_id 6 for calling a global function with example usage: test_predict_proba(mocker, evidence_type, expected_probas) and returns: np