class_docstr: Hard-sigmoid gradient function.
class_docstr_tok: ["Hard-sigmoid","gradient","function","."]
code: def forward_cpu(self, inputs):
      (gy,) = inputs
      gx = ((-2.5 < self.x) & (self.x < 2.5)) * gy * 0.2
      return (utils.force_array(gx, self.x.dtype),)
  
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 3 for calling the HardSigmoidGrad obj's underlying member method code with example usage: obj.forward_cpu(inputs) and returns: utils