class_docstr: Logistic sigmoid gradient function.
class_docstr_tok: ["Logistic","sigmoid","gradient","function","."]
code: def forward_cpu(self, inputs):
      self.retain_inputs((0, 1))
      y, gy = inputs
      one = y.dtype.type(1)
      return (utils.force_array(gy * y * (one - y)),)
  
imports: import numpy
  import chainer
  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 SigmoidGrad obj's underlying member method code with example usage: obj.forward_cpu(inputs) and returns: utils