class_docstr: Hard-sigmoid function.
class_docstr_tok: ["Hard-sigmoid","function","."]
code: def forward_gpu(self, inputs):
      x = inputs[0]
      self.retain_inputs((0,))
      return (
          cuda.elementwise(
              "T x",
              "T y",
              "y = min(1.0, max(0.0, x * 0.2 + 0.5))",
              "hard_sigmoid_fwd",
          )(x),
      )
  
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 HardSigmoid obj's underlying member method code with example usage: obj.forward_gpu(inputs) and returns: cuda