class_docstr: Logistic sigmoid gradient function.
class_docstr_tok: ["Logistic","sigmoid","gradient","function","."]
code: def backward(self, indexes, grad_outputs):
      y, gy = self.get_retained_inputs()
      (ggx,) = grad_outputs
      return ggx * gy * (1 - 2 * y), ggx * y * (1 - 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 5 for calling the SigmoidGrad obj's underlying member method code with example usage: obj.backward(indexes, grad_outputs) and returns: unknown,  unknown