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: Refits every learner with a dataset bootstrapped from its training instances. Contrary to .bag(), it bootstraps
  the training data for each learner based on its own examples.
  Todo:
      Where is .bag()?
  Args:
      **fit_kwargs: Keyword arguments to be passed to the fit method of the predictor.
docstr_tok: ["Refits","every","learner","with","a","dataset","bootstrapped","from","its","training","instances",".","Contrary","to",".bag","(",")",",","it","bootstraps","the","training","data","for","each","learner","based","on","its","own","examples",".","Todo",":","Where","is",".bag","(",")","?","Args",":","*","*","fit_kwargs",":","Keyword","arguments","to","be","passed","to","the","fit","method","of","the","predictor","."]
code: def rebag(self, **fit_kwargs) -> None:
      """
      Refits every learner with a dataset bootstrapped from its training instances. Contrary to .bag(), it bootstraps
      the training data for each learner based on its own examples.
      Todo:
          Where is .bag()?
      Args:
          **fit_kwargs: Keyword arguments to be passed to the fit method of the predictor.
      """
      self._fit_to_known(bootstrap=True, **fit_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 10 for calling the BaseCommittee obj's underlying member method code with example usage: obj.rebag() without return types