class_docstr: Learning fair representations is a pre-processing technique that finds a
  latent representation which encodes the data well but obfuscates information
  about protected attributes [2]_.
  References:
      .. [2] R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork,  "Learning
         Fair Representations." International Conference on Machine Learning,
         2013.
  Based on code from https://github.com/zjelveh/learning-fair-representations
docstr: Fit and transform methods sequentially.
  
  Args:
      dataset (BinaryLabelDataset): Dataset containing labels that needs to be transformed.
      maxiter (int): Maximum number of iterations.
      maxfun (int): Maxinum number of function evaluations.
      threshold(float, optional): threshold parameter used for binary label prediction.
  Returns:
      dataset (BinaryLabelDataset): Transformed Dataset.
code: def fit_transform(
      self, dataset, maxiter=5000, maxfun=5000, threshold=0.5
  ):
      """Fit and transform methods sequentially.
  
      Args:
          dataset (BinaryLabelDataset): Dataset containing labels that needs to be transformed.
          maxiter (int): Maximum number of iterations.
          maxfun (int): Maxinum number of function evaluations.
          threshold(float, optional): threshold parameter used for binary label prediction.
      Returns:
          dataset (BinaryLabelDataset): Transformed Dataset.
      """
  
      return self.fit(
          dataset, maxiter=maxiter, maxfun=maxfun
      ).transform(dataset, threshold=threshold)
  
file_path: AIF360/aif360/algorithms/preprocessing/lfr.py
imports: import numpy
  import scipy.optimize
  from aif360.algorithms import Transformer
  from aif360.algorithms.preprocessing.lfr_helpers import helpers
label_desc: Use image node_id 4 for calling the LFR obj's underlying member method code with example usage: obj.fit_transform(dataset, maxiter, maxfun, threshold) and returns: self