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: Interface for the score method of the predictor.
  
  Args:
      X: The samples for which prediction accuracy is to be calculated.
      y: Ground truth labels for X.
      **score_kwargs: Keyword arguments to be passed to the .score() method of the predictor.
  
  Returns:
      The score of the predictor.
docstr_tok: ["Interface","for","the","score","method","of","the","predictor",".","Args",":","X",":","The","samples","for","which","prediction","accuracy","is","to","be","calculated",".","y",":","Ground","truth","labels","for","X",".","*","*","score_kwargs",":","Keyword","arguments","to","be","passed","to","the",".score","(",")","method","of","the","predictor",".","Returns",":","The","score","of","the","predictor","."]
code: def score(self, X: modALinput, y: modALinput, **score_kwargs) -> Any:
      """
      Interface for the score method of the predictor.
  
      Args:
          X: The samples for which prediction accuracy is to be calculated.
          y: Ground truth labels for X.
          **score_kwargs: Keyword arguments to be passed to the .score() method of the predictor.
  
      Returns:
          The score of the predictor.
      """
      return self.estimator.score(X, y, **score_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 8 for calling the BaseLearner obj's underlying member method code with example usage: obj.score(X, y) and returns: self