code: def _forward_xp_core(self, x, W, b, xp):
      ndim = self.ndim
      stride = self.stride
      pad = self.pad
      dilate = self.dilate
  
      # gcol: C_O, k_1, ..., k_N, n, d_1, ..., d_N
      gcol = xp.tensordot(W, x, (0, 1)).astype(x.dtype, copy=False)
      # Roll n, which is batch size, before the first.
      gcol = xp.rollaxis(gcol, ndim + 1)
  
      # y: n, C_O, d_1, d_2, ..., d_N
      if xp is numpy:
          y = conv_nd.col2im_nd_cpu(
              gcol, stride, pad, self.outs, dilate=dilate
          )
      else:
          y = conv_nd.col2im_nd_gpu(
              gcol, stride, pad, self.outs, dilate=dilate
          )
      if b is not None:
          b_shape = (1, -1) + (1,) * ndim
          y += b.reshape(b_shape)
  
      return (y,)
  
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 6 for calling the DeconvolutionND obj's underlying member method code with example usage: obj._forward_xp_core(x, W, b, xp) and returns: y