class_docstr: Hard-sigmoid gradient function.
class_docstr_tok: ["Hard-sigmoid","gradient","function","."]
code: def forward_gpu(self, inputs):
      (gy,) = inputs
      return (
          cuda.elementwise(
              "T x, T g",
              "T gx",
              "gx = fabs(x) < 2.5 ? 0.2 * g : 0",
              "hard_sigmoid_bwd",
          )(self.x, gy),
      )
  
file_path: chainer/chainer/functions/activation/hard_sigmoid.py
imports: import numpy
  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 4 for calling the HardSigmoidGrad obj's underlying member method code with example usage: obj.forward_gpu(inputs) and returns: cuda