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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 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
  )