code: def _forward_grouped_convolution_xp(self, x, W, b, xp):
      # G: group count
      # N: batch size
      # iC: input channels
      # oC: output channels
      G = self.groups
      N, iC = x.shape[:2]
      oC = W.shape[0]
      k_size = W.shape[2:]
      iCg = iC // G
      oCg = oC // G
      dims = len(k_size)
      if iC % G != 0:
          raise TypeError(
              "The number of groups must be "
              "a divisor of that of input channels"
          )
      if oC % G != 0:
          raise TypeError(
              "The number of groups must be "
              "a divisor of that of output channels"
          )
  
      xp = backend.get_array_module(x)
  
      # (N, iC, k_size..., o_size...)
      x = conv_nd.im2col_nd(
          x,
          k_size,
          self.stride,
          self.pad,
          cover_all=self.cover_all,
          dilate=self.dilate,
      )
      o_size = x.shape[-dims:]
  
      x = xp.rollaxis(x, 0, dims + 2)  # (iC, k_size..., N, o_size...)
      mul_len = iCg * utils.size_of_shape(k_size)
      x = x.reshape(G, mul_len, N * utils.size_of_shape(o_size))
  
      W = W.reshape(G, oCg, mul_len)
  
      # (G, oCg, N*o_size) = (G, oCg, iCg*k_size) @ (G, iCg*k_size, N*o_size)
      y = convolution_2d._matmul(W, x).astype(x.dtype, copy=False)
      y = y.reshape(oC, N, *o_size)
      y = xp.rollaxis(y, 1)  # (N, oC, o_size...)
      if b is not None:
          y += b.reshape(1, b.size, *((1,) * dims))
  
      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 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 ConvolutionND obj's underlying member method code with example usage: obj._forward_grouped_convolution_xp(x, W, b, xp) and returns: y