code: def backward(self, indexes, grad_outputs):
      x, gy = self.get_retained_inputs()
      (ggW,) = grad_outputs
  
      ret = []
      if 0 in indexes:
          x_shape = x.shape[2:]
          gx = chainer.functions.deconvolution_nd(
              gy,
              ggW,
              stride=self.stride,
              pad=self.pad,
              outsize=x_shape,
              groups=self.groups,
              dilate=self.dilate,
          )
          ret.append(gx)
      if 1 in indexes:
          ggy = convolution_nd(
              x,
              ggW,
              stride=self.stride,
              pad=self.pad,
              cover_all=self.cover_all,
              groups=self.groups,
              dilate=self.dilate,
          )
          ret.append(ggy)
  
      return ret
  
imports: import numpy
  from six import moves
  import chainer
  from chainer import backend
  from chainer.backends import cuda
  from chainer import configuration
  from chainer import function_node
  from chainer.functions.connection import convolution_2d
  from chainer import utils
  from chainer.utils import conv
  from chainer.utils import conv_nd
  from chainer.utils import type_check
  import chainerx
label_desc: Use image node_id 8 for calling the ConvolutionNDGradW obj's underlying member method code with example usage: obj.backward(indexes, grad_outputs) and returns: ret