code: def backward(self, indexes, grad_outputs):
      x, W = self.get_retained_inputs()
      (gy,) = grad_outputs
  
      ret = []
      if 0 in indexes:
          x_shape = x.shape[2:]
          gx = chainer.functions.deconvolution_nd(
              gy,
              W,
              stride=self.stride,
              pad=self.pad,
              outsize=x_shape,
              dilate=self.dilate,
              groups=self.groups,
          )
          ret.append(gx)
      if 1 in indexes:
          (gW,) = ConvolutionNDGradW(self).apply((x, gy))
          ret.append(gW)
      if 2 in indexes:
          axis = (0,) + tuple(moves.range(2, gy.ndim))
          gb = chainer.functions.sum(gy, axis=axis)
          if gb.dtype != self.inputs[2].dtype:
              gb = chainer.functions.cast(gb, self.inputs[2].dtype)
          ret.append(gb)
  
      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 10 for calling the ConvolutionND obj's underlying member method code with example usage: obj.backward(indexes, grad_outputs) and returns: ret