code: def _forward_xp_core(self, x, W, b, xp):
      ndim = self.ndim
      ksize = W.shape[2:]
      stride = self.stride
      pad = self.pad
      dilate = self.dilate
  
      # Make patch array.
      if xp is numpy:
          col = conv_nd.im2col_nd_cpu(
              x,
              ksize,
              stride,
              pad,
              cover_all=self.cover_all,
              dilate=dilate,
          )
      else:
          col = conv_nd.im2col_nd_gpu(
              x,
              ksize,
              stride,
              pad,
              cover_all=self.cover_all,
              dilate=dilate,
          )
  
      # Compute correlation.
      axes = tuple(moves.range(1, ndim + 2))  # (1, 2, ..., N+1)
      y = xp.tensordot(col, W, (axes, axes)).astype(x.dtype, copy=False)
  
      # Apply bias if given.
      if b is not None:
          y += b
  
      # Roll c_O before the second in (n, y_1, y_2, ..., y_N, c_O).
      return (xp.rollaxis(y, ndim + 1, 1),)
  
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 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 7 for calling the ConvolutionND obj's underlying member method code with example usage: obj._forward_xp_core(x, W, b, xp) and returns: xp