code: def backward(self, indexes, grad_outputs):
      x, W = self.get_retained_inputs()
      if len(self.input_layouts) == 2:
          x_layout, _ = self.input_layouts
      else:
          x_layout, _, _ = self.input_layouts
      (gy,) = grad_outputs
  
      ret = []
      if 0 in indexes:
          _, _, xh, xw = x.shape
          gx = chainer.functions.deconvolution_2d(
              gy,
              W,
              stride=(self.sy, self.sx),
              pad=(self.ph, self.pw),
              outsize=(xh, xw),
              dilate=(self.dy, self.dx),
              groups=self.groups,
          )
          assert gx.shape == x.shape
          ret.append(gx)
      if 1 in indexes:
          (gW,) = Convolution2DGradW(
              self, W.shape, W.dtype, W.layout
          ).apply((x, gy))
          ret.append(gW)
      if 2 in indexes:
          axis = (0, 2, 3)
          inv_trans = memory_layouts._get_layout_transpose_axes(
              gy.ndim, None, x_layout
          )
          if inv_trans is None:
              raw_axis = axis
          else:
              raw_axis = tuple([inv_trans[i] for i in axis])
          gb = chainer.functions.sum(gy, axis=raw_axis)
          ret.append(gb)
  
      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 14 for calling the Convolution2DFunction obj's underlying member method code with example usage: obj.backward(indexes, grad_outputs) and returns: ret