code: def forward(self, inputs):
      self.retain_inputs((0, 1))  # only retain x and W
      x, W = inputs[:2]
      b = inputs[2] if len(inputs) == 3 else None
  
      if self.outs is None:
          dims = x.shape[2:]
          ksize = W.shape[2:]
          self.outs = tuple(
              conv.get_deconv_outsize(d, k, s, p, d=di)
              for d, k, s, p, di in zip(
                  dims, ksize, self.stride, self.pad, self.dilate
              )
          )
          assert all(
              out > 0 for out in self.outs
          ), "Output sizes should be positive."
      self._set_cover_all(x, W)
  
      xp = backend.get_array_module(*inputs)
      if xp is numpy:
          return self._forward_xp(x, W, b, numpy)
      elif not self._use_cudnn(x, W, b):
          return self._forward_xp(x, W, b, cuda.cupy)
      else:
          return self._forward_cudnn(x, W, b)
  
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.functions.connection import convolution_nd
  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 9 for calling the DeconvolutionND obj's underlying member method code with example usage: obj.forward(inputs) and returns: self, self, self