import tensorflow as tf

      should be used for the indicator function.
    scope: Optional scope for `name_scope`.
  Returns:
    loss: A `Tensor` of the same shape as `logits` with the component-wise loss.
    other_outputs: An empty dictionary, for consistency.
  Raises:
    ValueError: If `surrogate_type` is not `xent` or `hinge`.
  """
  with tf.name_scope(scope, 'roc_auc', [labels, logits, weights]):
    # Convert inputs to tensors and standardize dtypes.
    labels, logits, weights, original_shape = _prepare_labels_logits_weights(labels, logits, weights)

    # Create tensors of pairwise differences for logits and labels, and
    # pairwise products of weights. These have shape
    # [batch_size, batch_size, num_labels].
    logits_difference = tf.expand_dims(logits, 0) - tf.expand_dims(logits, 1)
