class_docstr: Hard-sigmoid gradient function.
class_docstr_tok: ["Hard-sigmoid","gradient","function","."]
code: def backward(self, indexes, grad_outputs):
      return HardSigmoidGrad(self.x).apply(grad_outputs)
  
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 5 for calling the HardSigmoidGrad obj's underlying member method code with example usage: obj.backward(indexes, grad_outputs) and returns: HardSigmoidGrad