class_docstr: Softplus gradient function.
class_docstr_tok: ["Softplus","gradient","function","."]
code: def forward_cpu(self, inputs):
      self.retain_inputs((0, 1))
      x, gy = inputs
      gx = (1 - 1 / (1 + numpy.exp(self.beta * x))) * gy
      return (utils.force_array(gx, x.dtype),)
  
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 3 for calling the SoftplusGrad obj's underlying member method code with example usage: obj.forward_cpu(inputs) and returns: utils