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: Args:
      tau (double, optional): Fairness penalty parameter.
      sensitive_attr (str, optional): Name of protected attribute.
      type (str, optional): The type of fairness metric to be used.
          Currently "fdr" (false discovery rate ratio) and "sr"
          (statistical rate/disparate impact) are supported. To use
          another type, the corresponding optimization class has to be
          implemented.
      seed (int, optional): Random seed.
code: def __init__(self, tau=0.8, sensitive_attr="", type="fdr", seed=None):
      """
      Args:
          tau (double, optional): Fairness penalty parameter.
          sensitive_attr (str, optional): Name of protected attribute.
          type (str, optional): The type of fairness metric to be used.
              Currently "fdr" (false discovery rate ratio) and "sr"
              (statistical rate/disparate impact) are supported. To use
              another type, the corresponding optimization class has to be
              implemented.
          seed (int, optional): Random seed.
      """
      super(MetaFairClassifier, self).__init__(
          tau=tau, sensitive_attr=sensitive_attr, type=type, seed=seed
      )
  
      self.tau = tau
      self.sensitive_attr = sensitive_attr
      if type == "fdr":
          self.obj = FalseDiscovery()
      elif type == "sr":
          self.obj = StatisticalRate()
      else:
          raise NotImplementedError(
              "Only 'fdr' and 'sr' are supported yet."
          )
      self.seed = seed
  
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 1 to create a new MetaFairClassifier object from inherited base classes: Transformer with example: obj = MetaFairClassifier(tau, sensitive_attr, type, seed)