class_docstr: Softplus gradient function.
class_docstr_tok: ["Softplus","gradient","function","."]
code: def forward_gpu(self, inputs):
      self.retain_inputs((0, 1))
      x, gy = inputs
      gx = cuda.elementwise(
          "T x, T gy, T beta",
          "T gx",
          "gx = (1 - 1 / (1 + exp(beta * x))) * gy",
          "softplus_bwd",
      )(x, gy, self.beta)
      return (gx,)
  
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 4 for calling the SoftplusGrad obj's underlying member method code with example usage: obj.forward_gpu(inputs) and returns: gx