code: def forward_chainerx(self, inputs):
      # TODO(imanishi): Support it
      if self.dy != 1 or self.dx != 1:
          return chainer.Fallback
      # TODO(imanishi): Support it
      if self.groups != 1:
          return chainer.Fallback
      # TODO(imanishi): Support it
      if any(a.dtype != inputs[0].dtype for a in inputs):
          return chainer.Fallback
      # TODO(imanishi): Support it
      self._calc_out_size(inputs[0].shape, inputs[1].shape)
      self._set_cover_all(inputs[0].shape, inputs[1].shape)
      if self.cover_all:
          return chainer.Fallback
  
      stride = (self.sy, self.sx)
      pad = (self.ph, self.pw)
      outsize = None if self.outh is None else (self.outh, self.outw)
  
      return (
          chainerx.conv_transpose(
              *inputs, stride=stride, pad=pad, outsize=outsize
          ),
      )
  
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 12 for calling the Deconvolution2DFunction obj's underlying member method code with example usage: obj.forward_chainerx(inputs) and returns: chainerx, chainer, chainer, chainer, chainer