code: def roc_auc_score(y_pred, y_true):
      """ROC AUC Score.
  
      Approximates the Area Under Curve score, using approximation based on
      the Wilcoxon-Mann-Whitney U statistic.
  
      Yan, L., Dodier, R., Mozer, M. C., & Wolniewicz, R. (2003).
      Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney Statistic.
  
      Measures overall performance for a full range of threshold levels.
  
      Arguments:
          y_pred: `Tensor`. Predicted values.
          y_true: `Tensor` . Targets (labels), a probability distribution.
  
      """
      with tf.name_scope("RocAucScore"):
          pos = tf.boolean_mask(y_pred, tf.cast(y_true, tf.bool))
          neg = tf.boolean_mask(y_pred, ~tf.cast(y_true, tf.bool))
  
          pos = tf.expand_dims(pos, 0)
          neg = tf.expand_dims(neg, 1)
  
          # original paper suggests performance is robust to exact parameter choice
          gamma = 0.2
          p = 3
  
          difference = tf.zeros_like(pos * neg) + pos - neg - gamma
  
          masked = tf.boolean_mask(difference, difference < 0.0)
  
          return tf.reduce_sum(tf.pow(-masked, p))
  
file_path: tflearn/tflearn/objectives.py
imports: from __future__ import division, print_function, absolute_import
  import tensorflow.compat.v1
  from .config import _EPSILON, _FLOATX
  from .utils import get_from_module
label_desc: Use image node_id 8 for calling a global function with example usage: roc_auc_score(y_pred, y_true) and returns: tf