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: Adds the new data and label to the known data for each learner, but does not retrain the model.
  Args:
      X: The new samples for which the labels are supplied by the expert.
      y: Labels corresponding to the new instances in X.
  Note:
      If the learners have been fitted, the features in X have to agree with the training samples which the
      classifier has seen.
docstr_tok: ["Adds","the","new","data","and","label","to","the","known","data","for","each","learner",",","but","does","not","retrain","the","model",".","Args",":","X",":","The","new","samples","for","which","the","labels","are","supplied","by","the","expert",".","y",":","Labels","corresponding","to","the","new","instances","in","X",".","Note",":","If","the","learners","have","been","fitted",",","the","features","in","X","have","to","agree","with","the","training","samples","which","the","classifier","has","seen","."]
code: def _add_training_data(self, X: modALinput, y: modALinput) -> None:
      """
      Adds the new data and label to the known data for each learner, but does not retrain the model.
      Args:
          X: The new samples for which the labels are supplied by the expert.
          y: Labels corresponding to the new instances in X.
      Note:
          If the learners have been fitted, the features in X have to agree with the training samples which the
          classifier has seen.
      """
      for learner in self.learner_list:
          learner._add_training_data(X, y)
  
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 4 for calling the BaseCommittee obj's underlying member method code with example usage: obj._add_training_data(X, y) without return types