code: def _predict_proba(self, X):
      if len(self.classes_) != 2:
          raise NotImplementedError(
              "predict_proba only supported"
              " for binary classification"
          )
      if self.loss == "log":
          df = self.decision_function(X).ravel()
          prob = scipy.special.expit(df)
      elif self.loss == "modified_huber":
          df = self.decision_function(X).ravel()
          prob = np.minimum(1, np.maximum(-1, df))
          prob += 1
          prob /= 2
      else:
          raise NotImplementedError(
              "predict_proba only supported when"
              " loss='log' or loss='modified_huber' "
              "(%s given)" % self.loss
          )
  
      out = np.zeros((X.shape[0], 2), dtype=np.float64)
      out[:, 1] = prob
      out[:, 0] = 1 - prob
  
      return out
  
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 2 for calling the BaseClassifier obj's underlying member method code with example usage: obj._predict_proba(X) and returns: out