docs: def log_deferred(op, log_id, every_n=1, first_n=None):
    """Helper method inserting compliance logging ops.
  
    Note: This helper is not guaranteed to be efficient, as it will insert ops
          and control dependencies. If this proves to be a bottleneck, submitters
          may wish to consider other methods such as extracting values from an
          .events file.
  
    Args:
      op: A tf op to be printed.
      log_id: a uuid provided by the logger in mlperf_log.py
      every_n: If repeat is True, with what frequency should the input op be '
               logged. If repeat is False, this argument is ignored.
      first_n: Only log this many values. This arg does not interact with every_n.
               The first_n refers to the first n that would have been logged.
    """
  
    prefix = ":::MLPv0.5.0 [{}]".format(log_id)
    if not first_n is not None and first_n == 1:
      return tf.Print(op, [tf.timestamp(), op], message=prefix, first_n=1)
  
    counter = tf.Variable(tf.zeros(shape=(), dtype=tf.int32) - 1,
                          aggregation=tf.VariableAggregation.MEAN)
    increment = tf.assign_add(counter, 1, use_locking=True)
    return tf.cond(
        tf.equal(tf.mod(increment, every_n), 0),
        lambda :tf.Print(op, [tf.timestamp(), op], message=prefix,
                         first_n=first_n),
        lambda :op
    )