code: def forward_cpu(self, inputs):
      self.retain_inputs((0, 1, 2))
      x, beta, gy = inputs
      beta = beta.reshape(self.extended_shape)
  
      sig = _sigmoid(beta * x)
      y = x * sig
      by = beta * y
      one = x.dtype.type(1)
  
      gx = gy * (by + sig * (one - by))
      gb = gy * y * (x - y)
      gb = utils.force_array(gb.sum(axis=self.reduction_axes))
      return gx, gb
  
imports: import numpy
  import chainer
  from chainer.backends import cuda
  from chainer import function_node
  from chainer import utils
  from chainer.utils import type_check
label_desc: Use image node_id 2 for calling the SwishGrad obj's underlying member method code with example usage: obj.forward_cpu(inputs) and returns: gx,  gb