class_docstr: Hard-sigmoid function.
class_docstr_tok: ["Hard-sigmoid","function","."]
code: def backward(self, indexes, grad_outputs):
      (x,) = self.get_retained_inputs()
      return HardSigmoidGrad(x.data).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 4 for calling the HardSigmoid obj's underlying member method code with example usage: obj.backward(indexes, grad_outputs) and returns: HardSigmoidGrad