class_docstr: This class is an abstract model of a committee-based active learning algorithm.
  Args:
      learner_list: A list of ActiveLearners forming the Committee.
      query_strategy: Query strategy function. Committee supports disagreement-based query strategies from
          :mod:`modAL.disagreement`, but uncertainty-based ones from :mod:`modAL.uncertainty` are also supported.
      on_transformed: Whether to transform samples with the pipeline defined by each learner's estimator
          when applying the query strategy.
  Attributes:
      classes_: Class labels known by the Committee.
      n_classes_: Number of classes known by the Committee.
  Examples:
      >>> from sklearn.datasets import load_iris
      >>> from sklearn.neighbors import KNeighborsClassifier
      >>> from sklearn.ensemble import RandomForestClassifier
      >>> from modAL.models import ActiveLearner, Committee
      >>>
      >>> iris = load_iris()
      >>>
      >>> # initialize ActiveLearners
      >>> learner_1 = ActiveLearner(
      ...     estimator=RandomForestClassifier(),
      ...     X_training=iris['data'][[0, 50, 100]], y_training=iris['target'][[0, 50, 100]]
      ... )
      >>> learner_2 = ActiveLearner(
      ...     estimator=KNeighborsClassifier(n_neighbors=3),
      ...     X_training=iris['data'][[1, 51, 101]], y_training=iris['target'][[1, 51, 101]]
      ... )
      >>>
      >>> # initialize the Committee
      >>> committee = Committee(
      ...     learner_list=[learner_1, learner_2]
      ... )
      >>>
      >>> # querying for labels
      >>> query_idx, query_sample = committee.query(iris['data'])
      >>>
      >>> # ...obtaining new labels from the Oracle...
      >>>
      >>> # teaching newly labelled examples
      >>> committee.teach(
      ...     X=iris['data'][query_idx].reshape(1, -1),
      ...     y=iris['target'][query_idx].reshape(1, )
      ... )
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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.
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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.
      """
      super().fit(X, y, **fit_kwargs)
      self._set_classes()
  
imports: from typing import Any, Callable, List, Optional, Tuple
  import numpy
  from modAL.acquisition import max_EI
  from modAL.disagreement import max_std_sampling, vote_entropy_sampling
  from modAL.models.base import BaseCommittee, BaseLearner
  from modAL.uncertainty import uncertainty_sampling
  from modAL.utils.data import data_vstack, modALinput, retrieve_rows
  from modAL.utils.validation import check_class_labels, check_class_proba
  from sklearn.base import BaseEstimator
  from sklearn.metrics import accuracy_score
  from sklearn.utils import check_X_y
label_desc: Use image node_id 4 for calling the Committee obj's underlying member method code with example usage: obj.fit(X, y) without return types