class_docstr: Softplus gradient function.
class_docstr_tok: ["Softplus","gradient","function","."]
code: def backward(self, indexes, grad_outputs):
      x, gy = self.get_retained_inputs()
      (ggx,) = grad_outputs
      e = chainer.functions.exp(self.beta * x)
      gx = ggx * gy * self.beta * e / (1 + e) ** 2
      ggy = SoftplusGrad((self.beta,)).apply((x, ggx))[0]
      return gx, ggy
  
imports: import numpy
  from chainer.backends import cuda
  from chainer import function_node
  import chainer.functions
  from chainer import utils
  from chainer.utils import type_check
label_desc: Use image node_id 5 for calling the SoftplusGrad obj's underlying member method code with example usage: obj.backward(indexes, grad_outputs) and returns: gx,  ggy