class_docstr: Hard-sigmoid gradient function.
class_docstr_tok: ["Hard-sigmoid","gradient","function","."]
code: def check_type_forward(self, in_types):
      type_check._argname(in_types, ("gy",))
  
      type_check.expect(
          in_types[0].dtype.kind == "f",
          in_types[0].dtype == 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 2 for calling the HardSigmoidGrad obj's underlying member method code with example usage: obj.check_type_forward(in_types) without return types