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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def roc_auc(logits, labels, weights_fn=None):
 """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 """

  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)