code: def predict_log_proba(self, X):
      proba = self.predict_proba(X)
  
      if self.n_outputs_ == 1:
          return np.log(proba)
  
      for k in range(self.n_outputs_):
          proba[k] = np.log(proba[k])
  
      return proba
  
imports: import numbers
  import numpy
  from sklearn import preprocessing
  from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
  from sklearn.utils import check_random_state
  from sklearn.utils.multiclass import check_classification_targets
  from sklearn.utils.validation import check_array, check_is_fitted, check_X_y
  import daal4py
  from .._utils import control_n_jobs, getFPType, run_with_n_jobs
label_desc: Use image node_id 5 for calling the GBTDAALClassifier obj's underlying member method code with example usage: obj.predict_log_proba(X) and returns: proba, np