code: def _learning_rate(hp, t):
      if t == 0:
          raise RuntimeError(
              "Can't determine the learning rate of Adam optimizer "
              "because the update steps have not been started."
          )
      fix1 = 1.0 - math.pow(hp.beta1, t)
      fix2 = 1.0 - math.pow(hp.beta2, t)
      return hp.alpha * math.sqrt(fix2) / fix1
  
imports: from __future__ import division
  import math
  import warnings
  import numpy
  import chainer
  from chainer.backends import cuda
  from chainer.backends import intel64
  from chainer import optimizer
  from chainer import types
label_desc: Use image node_id 1 for calling a global function with example usage: _learning_rate(hp, t) and returns: unknown