class_docstr: Adversarial debiasing is an in-processing technique that learns a
  classifier to maximize prediction accuracy and simultaneously reduce an
  adversary's ability to determine the protected attribute from the
  predictions [5]_. This approach leads to a fair classifier as the
  predictions cannot carry any group discrimination information that the
  adversary can exploit.
  
  References:
      .. [5] B. H. Zhang, B. Lemoine, and M. Mitchell, "Mitigating Unwanted
         Biases with Adversarial Learning," AAAI/ACM Conference on Artificial
         Intelligence, Ethics, and Society, 2018.
docstr: Compute the adversary predictions for the protected attribute.
          
code: def _adversary_model(self, pred_logits, true_labels):
      """Compute the adversary predictions for the protected attribute."""
      with tf.variable_scope("adversary_model"):
          c = tf.get_variable("c", initializer=tf.constant(1.0))
          s = tf.sigmoid((1 + tf.abs(c)) * pred_logits)
  
          W2 = tf.get_variable(
              "W2",
              [3, 1],
              initializer=tf.initializers.glorot_uniform(
                  seed=self.seed4
              ),
          )
          b2 = tf.Variable(tf.zeros(shape=[1]), name="b2")
  
          pred_protected_attribute_logit = (
              tf.matmul(
                  tf.concat(
                      [s, s * true_labels, s * (1.0 - true_labels)],
                      axis=1,
                  ),
                  W2,
              )
              + b2
          )
          pred_protected_attribute_label = tf.sigmoid(
              pred_protected_attribute_logit
          )
  
      return (
          pred_protected_attribute_label,
          pred_protected_attribute_logit,
      )
  
file_path: AIF360/aif360/algorithms/inprocessing/adversarial_debiasing.py
imports: import numpy
  from aif360.algorithms import Transformer
label_desc: Use image node_id 3 for calling the AdversarialDebiasing obj's underlying member method code with example usage: obj._adversary_model(pred_logits, true_labels) and returns: pred_protected_attribute_label,  pred_protected_attribute_logit