code: def _predict_proba_for_estimator(X, estimator, interval, lag):
      """Private function used to predict class probabilities in parallel."""
      transformed_x = _transform(X, interval, lag)
      return estimator.predict_proba(transformed_x)
  
imports: import numpy
  from joblib import Parallel, delayed
  from sklearn.base import clone
  from sklearn.ensemble._base import _partition_estimators
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils.validation import check_random_state
  from sktime.classification.base import BaseClassifier
label_desc: Use image node_id 3 for calling a global function with example usage: _predict_proba_for_estimator(X, estimator, interval, lag) and returns: estimator