code: def backward(self, indexes, grad_outputs):
      x, W = self.get_retained_inputs()
      if len(self.input_layouts) == 2:
          x_layout, w_layout = self.input_layouts
      else:
          x_layout, w_layout, _ = self.input_layouts
      (gy,) = grad_outputs
  
      ret = []
      if 0 in indexes:
          if self.cover_all is None:
              self._set_cover_all(x.shape, W.shape)
          gx = chainer.functions.convolution_2d(
              gy,
              W,
              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(gx)
      if 1 in indexes:
          if self.cover_all is None:
              self._set_cover_all(x.shape, W.shape)
          (gW,) = convolution_2d.Convolution2DGradW(
              self, W.shape, W.dtype, w_layout
          ).apply((gy, x))
          ret.append(gW)
      if 2 in indexes:
          gb = chainer.functions.sum(gy, axis=(0, 2, 3))
          ret.append(gb)
  
      return ret
  
imports: import numpy
  import chainer
  from chainer.backends import cuda
  from chainer.backends import intel64
  from chainer import configuration
  from chainer import function_node
  import chainer.functions
  from chainer.functions.connection import convolution_2d
  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 13 for calling the Deconvolution2DFunction obj's underlying member method code with example usage: obj.backward(indexes, grad_outputs) and returns: ret