code: def predict_proba(estimator, X):
      # type: (Any, Any) -> Optional[np.ndarray]
      """Return result of predict_proba, if an estimator supports it, or None."""
      if is_probabilistic_classifier(estimator):
          try:
              (proba,) = estimator.predict_proba(X)
              return proba
          except NotImplementedError:
              return None
      else:
          return None
  
imports: from __future__ import absolute_import
  from distutils.version import LooseVersion
  from typing import Any, Optional, List, Tuple
  import numpy
  import scipy.sparse
  from sklearn.multiclass import OneVsRestClassifier
  from eli5.sklearn.unhashing import invert_hashing_and_fit, handle_hashing_vec
  from eli5._feature_names import FeatureNames
label_desc: Use image node_id 4 for calling a global function with example usage: predict_proba(estimator, X) and returns: None, proba, None