class_docstr: Core abstraction in modAL.
  
  Args:
      estimator: The estimator to be used in the active learning loop.
      query_strategy: Function providing the query strategy for the active learning loop,
          for instance, modAL.uncertainty.uncertainty_sampling.
      force_all_finite: When True, forces all values of the data finite.
          When False, accepts np.nan and np.inf values.
      on_transformed: Whether to transform samples with the pipeline defined by the estimator
          when applying the query strategy.
      **fit_kwargs: keyword arguments.
  
  Attributes:
      estimator: The estimator to be used in the active learning loop.
      query_strategy: Function providing the query strategy for the active learning loop.
docstr: Class probabilities if the predictor is a classifier. Interface with the predict_proba method of the classifier.
  
  Args:
      X: The samples for which the class probabilities are to be predicted.
      **predict_proba_kwargs: Keyword arguments to be passed to the predict_proba method of the classifier.
  
  Returns:
      Class probabilities for X.
docstr_tok: ["Class","probabilities","if","the","predictor","is","a","classifier",".","Interface","with","the","predict_proba","method","of","the","classifier",".","Args",":","X",":","The","samples","for","which","the","class","probabilities","are","to","be","predicted",".","*","*","predict_proba_kwargs",":","Keyword","arguments","to","be","passed","to","the","predict_proba","method","of","the","classifier",".","Returns",":","Class","probabilities","for","X","."]
code: def predict_proba(self, X: modALinput, **predict_proba_kwargs) -> Any:
      """
      Class probabilities if the predictor is a classifier. Interface with the predict_proba method of the classifier.
  
      Args:
          X: The samples for which the class probabilities are to be predicted.
          **predict_proba_kwargs: Keyword arguments to be passed to the predict_proba method of the classifier.
  
      Returns:
          Class probabilities for X.
      """
      return self.estimator.predict_proba(X, **predict_proba_kwargs)
  
imports: import abc
  import sys
  import warnings
  from typing import Any, Callable, Iterator, List, Tuple, Union
  import numpy
  import scipy.sparse
  from modAL.utils.data import data_hstack, modALinput, retrieve_rows
  from sklearn.base import BaseEstimator
  from sklearn.ensemble._base import _BaseHeterogeneousEnsemble
  from sklearn.pipeline import Pipeline
label_desc: Use image node_id 6 for calling the BaseLearner obj's underlying member method code with example usage: obj.predict_proba(X) and returns: self