class_docstr: Logistic sigmoid function.
class_docstr_tok: ["Logistic","sigmoid","function","."]
code: def backward(self, indexes, grad_outputs):
      if self._use_cudnn:
          x = self.get_retained_inputs()[0].data
      else:
          x = None
      y = self.get_retained_outputs()[0]
      (gy,) = grad_outputs
      return SigmoidGrad((x,)).apply((y, gy))
  
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 4 for calling the Sigmoid obj's underlying member method code with example usage: obj.backward(indexes, grad_outputs) and returns: SigmoidGrad