class_docstr: A collection of algorithms for construction of fair ranked candidate lists. [1]_ .
  
  References:
      .. [1] Sahin Cem Geyik, Stuart Ambler, and Krishnaram Kenthapadi,
          "Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search,"
          KDD '19: Proceedings of the 25th ACM SIGKDD International Conference
          on Knowledge Discovery & Data Mining, July 2019, Pages 2221-2231.
code: def fit_predict(
      self,
      dataset: RegressionDataset,
      rec_size: int,
      target_prop: dict,
      rerank_type: str = "Constrained",
      renormalize_scores: bool = False,
  ) -> RegressionDataset:
      self.fit(dataset=dataset)
      return self.predict(
          dataset=dataset,
          rec_size=rec_size,
          target_prop=target_prop,
          rerank_type=rerank_type,
          renormalize_scores=renormalize_scores,
      )
  
file_path: AIF360/aif360/algorithms/postprocessing/deterministic_reranking.py
imports: import numpy
  import pandas
  from aif360.algorithms import Transformer
  from aif360.datasets import StructuredDataset, RegressionDataset
label_desc: Use image node_id 4 for calling the DeterministicReranking obj's underlying member method code with example usage: obj.fit_predict(dataset, rec_size, target_prop, rerank_type, renormalize_scores) and returns: self