class_docstr: The meta algorithm here takes the fairness metric as part of the input
  and returns a classifier optimized w.r.t. that fairness metric [11]_.
  
  References:
      .. [11] L. E. Celis, L. Huang, V. Keswani, and N. K. Vishnoi.
         "Classification with Fairness Constraints: A Meta-Algorithm with
         Provable Guarantees," 2018.
docstr: Obtain the predictions for the provided dataset using the learned
  classifier model.
  
  Args:
      dataset (BinaryLabelDataset): Dataset containing labels that needs
          to be transformed.
  
  Returns:
      BinaryLabelDataset: Transformed dataset.
code: def predict(self, dataset):
      """Obtain the predictions for the provided dataset using the learned
      classifier model.
  
      Args:
          dataset (BinaryLabelDataset): Dataset containing labels that needs
              to be transformed.
  
      Returns:
          BinaryLabelDataset: Transformed dataset.
      """
      t = self.model(dataset.features)
  
      pred_dataset = dataset.copy()
      pred_dataset.labels = (t > 0).astype(int).reshape((-1, 1))
      pred_dataset.scores = ((t + 1) / 2).reshape((-1, 1))
  
      return pred_dataset
  
file_path: AIF360/aif360/algorithms/inprocessing/meta_fair_classifier.py
imports: import numpy
  from aif360.algorithms import Transformer
  from aif360.algorithms.inprocessing.celisMeta import FalseDiscovery
  from aif360.algorithms.inprocessing.celisMeta import StatisticalRate
label_desc: Use image node_id 3 for calling the MetaFairClassifier obj's underlying member method code with example usage: obj.predict(dataset) and returns: pred_dataset