code: def test_logistic_regression_preds_proba():
      """[Utils] LogisticRegression: check label predictions."""
  
      g = np.array(
          [
              [0.68335447, 0.62546744, 0.42995095],
              [0.66258275, 0.62136312, 0.45756156],
              [0.64116395, 0.61724135, 0.48543536],
              [0.61916698, 0.61310265, 0.51340005],
              [0.59666983, 0.60894755, 0.54128111],
              [0.57375858, 0.60477659, 0.56890605],
              [0.55052625, 0.60059033, 0.59610873],
              [0.5270714, 0.59638931, 0.62273319],
              [0.50349645, 0.59217411, 0.64863706],
              [0.47990593, 0.58794531, 0.67369429],
              [0.45640469, 0.58370348, 0.69779712],
              [0.43309595, 0.57944923, 0.72085727],
          ]
      )
  
      p = LogisticRegression().fit(X, z).predict_proba(X)
  
      np.testing.assert_array_almost_equal(p, g)
  
imports: import numpy
  import os
  from mlens.ensemble.base import Layer, Sequential
  from mlens.utils.dummy import OLS, LogisticRegression, Scale
  from mlens.testing.dummy import ESTIMATORS, PREPROCESSING, ESTIMATORS_PROBA, ECM, ECM_PROBA, InitMixin
  from mlens.testing.dummy import Data, EstimatorContainer
  from mlens.utils import assert_correct_format
  from mlens.utils.formatting import _assert_format, _format_instances
  from mlens.utils.exceptions import NotFittedError
  from mlens.externals.sklearn.base import BaseEstimator
label_desc: Use image node_id 9 for calling a global function with example usage: test_logistic_regression_preds_proba() without return types