def _SoftplusGradGrad(op, grad):
  # Let:
  #   y = tf.nn.softplus(x)
  #   dx = gen_nn_ops._softplus_grad(dy, x) = dy / (1 + exp(-x))
  # This op computes (ddy, d2x) from op.inputs == [dy, x] and grad == ddx.
  dy, x = op.inputs
  with ops.control_dependencies([grad.op]):
    ddy = gen_nn_ops._softplus_grad(grad, x)  # pylint: disable=protected-access
    d2x = grad * dy / (math_ops.exp(-x) + 2.0 + math_ops.exp(x))
    return (ddy, d2x)

def _SoftplusGrad(op, grad):
  return gen_nn_ops._softplus_grad(grad, op.inputs[0])

def _guided_grad_softplus(op, grad):
    return guided_grad(gen_nn_ops._softplus_grad(grad, op.outputs[0]))