class_docstr: Hard-sigmoid function.
class_docstr_tok: ["Hard-sigmoid","function","."]
code: def forward_cpu(self, inputs):
      x = inputs[0]
      y = numpy.clip(x * 0.2 + 0.5, 0.0, 1.0)
      self.retain_inputs((0,))
      return (utils.force_array(y, 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 2 for calling the HardSigmoid obj's underlying member method code with example usage: obj.forward_cpu(inputs) and returns: utils