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: Returns the output of the estimator's score function on the given
  test data and labels.
  
  Args:
      X (array-like): Test samples.
      y (array-like): True labels for X.
      sample_weight (array-like, optional): Sample weights.
  
  Returns:
      float: `self.estimator.score(X, y, sample_weight)`
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code: def score(self, X, y, sample_weight=None):
      """Returns the output of the estimator's score function on the given
      test data and labels.
  
      Args:
          X (array-like): Test samples.
          y (array-like): True labels for X.
          sample_weight (array-like, optional): Sample weights.
  
      Returns:
          float: `self.estimator.score(X, y, sample_weight)`
      """
      return self.estimator_.score(X, y, sample_weight=sample_weight)
  
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 8 for calling the ReweighingMeta obj's underlying member method code with example usage: obj.score(X, y, sample_weight) and returns: self