code: def roc_auc(logits, labels, weights_fn=None):
    
    del weights_fn
    with tf.variable_scope("roc_auc", values=[logits, labels]):
      predictions = tf.argmax(logits, axis=-1)
      _, auc = tf.metrics.auc(labels, predictions, curve="ROC")
      return auc, tf.constant(1.0)
docs: Calculate ROC AUC.
  
    Requires binary classes.
  
    Args:
      logits: Tensor of size [batch_size, 1, 1, num_classes]
      labels: Tensor of size [batch_size, 1, 1, num_classes]
      weights_fn: Function that takes in labels and weighs examples (unused)
    Returns:
      ROC AUC (scalar), weights