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: Probability estimates from ``self.estimator_``.
  
  The returned estimates for all classes are ordered by the label of
  classes.
  
  Args:
      X (array-like): Test samples.
  
  Returns:
      array: Returns the probability of the sample for each class in the
      model, where classes are ordered as they are in ``self.classes_``.
docstr_tok: ["Probability","estimates","from","``","self.estimator_","``",".","The","returned","estimates","for","all","classes","are","ordered","by","the","label","of","classes",".","Args",":","X","(","array-like",")",":","Test","samples",".","Returns",":","array",":","Returns","the","probability","of","the","sample","for","each","class","in","the","model",",","where","classes","are","ordered","as","they","are","in","``","self.classes_","``","."]
code: def predict_proba(self, X):
      """Probability estimates from ``self.estimator_``.
  
      The returned estimates for all classes are ordered by the label of
      classes.
  
      Args:
          X (array-like): Test samples.
  
      Returns:
          array: Returns the probability of the sample for each class in the
          model, where classes are ordered as they are in ``self.classes_``.
      """
      return self.estimator_.predict_proba(X)
  
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 6 for calling the ReweighingMeta obj's underlying member method code with example usage: obj.predict_proba(X) and returns: self