code: def _forward_gpu_core(self, x, W, b):
      # Implementation using col2im
      gcol = cuda.cupy.tensordot(W, x, (0, 1)).astype(
          x.dtype, copy=False
      )
      # - k, m, n: shape of out_channel
      # - b: number of inputs
      # - h, w: height and width of kernels
      # k, m, n, b, h, w -> b, k, m, n, h, w
      gcol = cuda.cupy.rollaxis(gcol, 3)
      y = conv.col2im_gpu(
          gcol,
          self.sy,
          self.sx,
          self.ph,
          self.pw,
          self.outh,
          self.outw,
          dy=self.dy,
          dx=self.dx,
      )
      if b is not None:
          y += b.reshape(1, b.size, 1, 1)
      return (y,)
  
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 9 for calling the Deconvolution2DFunction obj's underlying member method code with example usage: obj._forward_gpu_core(x, W, b) and returns: y