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 all learners to the given data and labels.
  Args:
      X: The new samples for which the labels are supplied by the expert.
      y: Labels corresponding to the new instances in X.
      bootstrap: If True, the method trains the model on a set bootstrapped from X.
      **fit_kwargs: Keyword arguments to be passed to the fit method of the predictor.
docstr_tok: ["Fits","all","learners","to","the","given","data","and","labels",".","Args",":","X",":","The","new","samples","for","which","the","labels","are","supplied","by","the","expert",".","y",":","Labels","corresponding","to","the","new","instances","in","X.","bootstrap",":","If","True",",","the","method","trains","the","model","on","a","set","bootstrapped","from","X",".","*","*","fit_kwargs",":","Keyword","arguments","to","be","passed","to","the","fit","method","of","the","predictor","."]
code: def _fit_on_new(
      self,
      X: modALinput,
      y: modALinput,
      bootstrap: bool = False,
      **fit_kwargs,
  ) -> None:
      """
      Fits all learners to the given data and labels.
      Args:
          X: The new samples for which the labels are supplied by the expert.
          y: Labels corresponding to the new instances in X.
          bootstrap: If True, the method trains the model on a set bootstrapped from X.
          **fit_kwargs: Keyword arguments to be passed to the fit method of the predictor.
      """
      for learner in self.learner_list:
          learner._fit_on_new(X, y, bootstrap=bootstrap, **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 6 for calling the BaseCommittee obj's underlying member method code with example usage: obj._fit_on_new(X, y, bootstrap) without return types