code: def predict_proba(self):
      if self.loss not in ("log", "modified_huber"):
          raise AttributeError(
              "predict_proba only supported when"
              " loss='log' or loss='modified_huber' "
              "(%s given)" % self.loss
          )
      return self._predict_proba
  
imports: import numpy
  import scipy.sparse
  import scipy.special
  from sklearn.base import BaseEstimator
  from sklearn.base import ClassifierMixin, RegressorMixin
  from sklearn.utils.extmath import safe_sparse_dot
  from sklearn.preprocessing import LabelBinarizer
  from sklearn.preprocessing import LabelEncoder
  from .randomkit import RandomState
label_desc: Use image node_id 1 for calling the BaseClassifier obj's underlying member method code with example usage: obj.predict_proba() and returns: self