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 classifier predictions for the outcome variable.
          
code: def _classifier_model(self, features, features_dim, keep_prob):
      """Compute the classifier predictions for the outcome variable."""
      with tf.variable_scope("classifier_model"):
          W1 = tf.get_variable(
              "W1",
              [features_dim, self.classifier_num_hidden_units],
              initializer=tf.initializers.glorot_uniform(
                  seed=self.seed1
              ),
          )
          b1 = tf.Variable(
              tf.zeros(shape=[self.classifier_num_hidden_units]),
              name="b1",
          )
  
          h1 = tf.nn.relu(tf.matmul(features, W1) + b1)
          h1 = tf.nn.dropout(h1, keep_prob=keep_prob, seed=self.seed2)
  
          W2 = tf.get_variable(
              "W2",
              [self.classifier_num_hidden_units, 1],
              initializer=tf.initializers.glorot_uniform(
                  seed=self.seed3
              ),
          )
          b2 = tf.Variable(tf.zeros(shape=[1]), name="b2")
  
          pred_logit = tf.matmul(h1, W2) + b2
          pred_label = tf.sigmoid(pred_logit)
  
      return pred_label, pred_logit
  
file_path: AIF360/aif360/algorithms/inprocessing/adversarial_debiasing.py
imports: import numpy
  from aif360.algorithms import Transformer
label_desc: Use image node_id 2 for calling the AdversarialDebiasing obj's underlying member method code with example usage: obj._classifier_model(features, features_dim, keep_prob) and returns: pred_label,  pred_logit