Tensorflow implementation of Mean Reciprocal Rank (mrr) metric compatible with tf.Estimator

mrr_metric.py

import tensorflow as tf

def mrr_metric(labels, predictions, weights=None,
              metrics_collections=None,
              updates_collections=None,
              name=None):
    
    with tf.name_scope(name, 'mrr_metric', [predictions, labels, weights]) as scope:

    
    	k = predictions.get_shape().as_list()[-1]
        _, r = tf.nn.top_k(predictions, k)

        get_ranked_indicies = tf.expand_dims(tf.where(tf.equal(tf.cast(r,tf.int64),labels))[:,1],1)

        rr = 1/(get_ranked_indicies+1)
        m_rr, update_mrr_op = tf.metrics.mean(rr, weights=weights, name=name)

        if metrics_collections:
            tf.add_to_collection(metrics_collections, m_rr)

        if updates_collections:
            tf.add_to_collections(updates_collections, update_mrr_op)

        return m_rr, update_mrr_op

What's the runtime complexity of this?