class_docstr: Base class for query-by-committee setup.
  Args:
      learner_list: List of ActiveLearner objects to form committee.
      query_strategy: Function to query labels.
      on_transformed: Whether to transform samples with the pipeline defined by each learner's estimator
          when applying the query strategy.
docstr: Fits every learner to a subset sampled with replacement from X. Calling this method makes the learner forget the
  data it has seen up until this point and replaces it with X! If you would like to perform bootstrapping on each
  learner using the data it has seen, use the method .rebag()!
  Calling this method makes the learner forget the data it has seen up until this point and replaces it with X!
  Args:
      X: The samples to be fitted on.
      y: The corresponding labels.
      **fit_kwargs: Keyword arguments to be passed to the fit method of the predictor.
docstr_tok: ["Fits","every","learner","to","a","subset","sampled","with","replacement","from","X",".","Calling","this","method","makes","the","learner","forget","the","data","it","has","seen","up","until","this","point","and","replaces","it","with","X","!","If","you","would","like","to","perform","bootstrapping","on","each","learner","using","the","data","it","has","seen",",","use","the","method",".rebag","(",")","!","Calling","this","method","makes","the","learner","forget","the","data","it","has","seen","up","until","this","point","and","replaces","it","with","X","!","Args",":","X",":","The","samples","to","be","fitted","on",".","y",":","The","corresponding","labels",".","*","*","fit_kwargs",":","Keyword","arguments","to","be","passed","to","the","fit","method","of","the","predictor","."]
code: def fit(
      self, X: modALinput, y: modALinput, **fit_kwargs
  ) -> "BaseCommittee":
      """
      Fits every learner to a subset sampled with replacement from X. Calling this method makes the learner forget the
      data it has seen up until this point and replaces it with X! If you would like to perform bootstrapping on each
      learner using the data it has seen, use the method .rebag()!
      Calling this method makes the learner forget the data it has seen up until this point and replaces it with X!
      Args:
          X: The samples to be fitted on.
          y: The corresponding labels.
          **fit_kwargs: Keyword arguments to be passed to the fit method of the predictor.
      """
      for learner in self.learner_list:
          learner.fit(X, y, **fit_kwargs)
  
      return self
  
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 7 for calling the BaseCommittee obj's underlying member method code with example usage: obj.fit(X, y) and returns: self