human
I wrote the below code

    if ((decay_type is None) or (decay_type == u'')):
        return None
    start_decay_at = tf.to_int32(start_decay_at)
    stop_decay_at = tf.to_int32(stop_decay_at)
    def decay_fn(learning_rate, global_step):
        u'The    computed    learning    rate    decay    function.\n                '
        global_step = tf.to_int32(global_step)
        decay_type_fn = getattr(tf.train, decay_type)
        decayed_learning_rate = decay_type_fn(learning_rate=learning_rate, global_step=(tf.minimum(global_step, stop_decay_at) - start_decay_at), decay_steps=decay_steps, decay_rate=decay_rate, staircase=staircase, name=u'decayed_learning_rate')
        final_lr = tf.train.piecewise_constant(x=global_step, boundaries=[start_decay_at], values=[learning_rate, decayed_learning_rate])
        if min_learning_rate:
            final_lr = tf.maximum(final_lr, min_learning_rate)
        return final_lr
    return decay_fn

What's a good function header?
 Here's a good function header?
assistant
#Here's a good function header.

def create_learning_rate_decay_fn(decay_type, decay_steps, decay_rate, start_decay_at=0, stop_decay_at=1000000000.0, min_learning_rate=None, staircase=False):