code: def backward(self, indexes, grad_outputs):
      x, gy = self.get_retained_inputs()
      (ggW,) = grad_outputs
  
      ret = []
      if 0 in indexes:
          xh, xw = x.shape[2:]
          gx = chainer.functions.deconvolution_2d(
              gy,
              ggW,
              stride=(self.sy, self.sx),
              pad=(self.ph, self.pw),
              outsize=(xh, xw),
              dilate=(self.dy, self.dx),
              groups=self.groups,
          )
          ret.append(gx)
      if 1 in indexes:
          ggy = convolution_2d(
              x,
              ggW,
              stride=(self.sy, self.sx),
              pad=(self.ph, self.pw),
              cover_all=self.cover_all,
              dilate=(self.dy, self.dx),
              groups=self.groups,
          )
          ret.append(ggy)
  
      return ret
  
imports: import numpy
  import chainer
  from chainer import backend
  from chainer.backends import cuda
  from chainer.backends import intel64
  from chainer import configuration
  from chainer import function_node
  import chainer.functions
  from chainer import memory_layouts
  from chainer.utils import argument
  from chainer.utils import conv
  from chainer.utils import type_check
  import chainerx
label_desc: Use image node_id 10 for calling the Convolution2DGradW obj's underlying member method code with example usage: obj.backward(indexes, grad_outputs) and returns: ret