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_`.
class_docstr_tok: ["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_","`","."]
docstr: Class labels from the base estimator.
docstr_tok: ["Class","labels","from","the","base","estimator","."]
code: def classes_(self):
      """Class labels from the base estimator."""
      return self.estimator_.classes_
  
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 3 for calling the ReweighingMeta obj's underlying member method code with example usage: obj.classes_() and returns: self