code: def _forward_xp_core(self, x, gy, xp):
      # Compute filter weight gradient.
      # (n, _, out_1, out_2, ..., out_N)
      out_axes = (0,) + tuple(moves.range(2, self.ndim + 2))
      # (n, _, _, ..., _, out_1, out_2, ..., out_N)
      col_axes = (0,) + tuple(
          moves.range(self.ndim + 2, self.ndim * 2 + 2)
      )
  
      # NumPy raises an error when the array is not contiguous.
      # See: https://github.com/chainer/chainer/issues/2744
      # TODO(niboshi): Remove this code when NumPy is fixed.
      if (
          xp is numpy
          and not (gy.flags.c_contiguous or gy.flags.f_contiguous)
          and 1 in gy.shape
      ):
          gy = numpy.ascontiguousarray(gy)
  
      if xp is numpy:
          col = conv_nd.im2col_nd_cpu(
              x,
              self.ksize,
              self.stride,
              self.pad,
              cover_all=self.cover_all,
              dilate=self.dilate,
          )
      else:
          col = conv_nd.im2col_nd_gpu(
              x,
              self.ksize,
              self.stride,
              self.pad,
              cover_all=self.cover_all,
              dilate=self.dilate,
          )
      gW = xp.tensordot(gy, col, (out_axes, col_axes)).astype(
          self.W_dtype, copy=False
      )
      return (gW,)
  
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 6 for calling the ConvolutionNDGradW obj's underlying member method code with example usage: obj._forward_xp_core(x, gy, xp) and returns: gW