class_docstr: Logistic sigmoid gradient function.
class_docstr_tok: ["Logistic","sigmoid","gradient","function","."]
code: def forward_gpu(self, inputs):
      self.retain_inputs((0, 1))
      y, gy = inputs
      if (
          chainer.should_use_cudnn("==always")
          and gy.flags.c_contiguous
          and self.x is not None
          and self.x.flags.c_contiguous
      ):
          gx = cudnn.activation_backward(self.x, y, gy, _mode)
      else:
          gx = cuda.elementwise(
              "T y, T gy",
              "T gx",
              "gx = gy * y * (1 - y)",
              "sigmoid_bwd",
          )(y, gy)
      return (gx,)
  
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 SigmoidGrad obj's underlying member method code with example usage: obj.forward_gpu(inputs) and returns: gx