class_docstr: A meta-estimator which wraps a given estimator with a reweighing
  preprocessing step.
  
  This is necessary for use in a Pipeline, etc.
  
  Attributes:
      estimator_ (sklearn.BaseEstimator): The fitted underlying estimator.
      reweigher_: The fitted underlying reweigher.
      classes_ (array, shape (n_classes,)): Class labels from `estimator_`.
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docstr: Args:
      estimator (sklearn.BaseEstimator): Estimator to be wrapped.
      reweigher (optional): Preprocessor which returns new sample weights
          from ``transform()``. If ``None``, defaults to
          :class:`~aif360.sklearn.preprocessing.Reweighing`.
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code: def __init__(self, estimator, reweigher=None):
      """
      Args:
          estimator (sklearn.BaseEstimator): Estimator to be wrapped.
          reweigher (optional): Preprocessor which returns new sample weights
              from ``transform()``. If ``None``, defaults to
              :class:`~aif360.sklearn.preprocessing.Reweighing`.
      """
      self.reweigher = reweigher
      self.estimator = estimator
  
imports: import numpy
  from sklearn.base import BaseEstimator, MetaEstimatorMixin, clone
  from sklearn.utils.metaestimators import if_delegate_has_method
  from sklearn.utils.validation import has_fit_parameter
  from aif360.sklearn.utils import check_inputs, check_groups
label_desc: Use image node_id 1 to create a new ReweighingMeta object from inherited base classes: BaseEstimator, MetaEstimatorMixin with example: obj = ReweighingMeta(estimator, reweigher)