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: Predict class labels for the given samples using ``self.estimator_``.
  
  Args:
      X (array-like): Test samples.
  
  Returns:
      array: Predicted class label per sample.
docstr_tok: ["Predict","class","labels","for","the","given","samples","using","``","self.estimator_","``",".","Args",":","X","(","array-like",")",":","Test","samples",".","Returns",":","array",":","Predicted","class","label","per","sample","."]
code: def predict(self, X):
      """Predict class labels for the given samples using ``self.estimator_``.
  
      Args:
          X (array-like): Test samples.
  
      Returns:
          array: Predicted class label per sample.
      """
      return self.estimator_.predict(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 5 for calling the ReweighingMeta obj's underlying member method code with example usage: obj.predict(X) and returns: self