code: def _forward_grouped_convolution_xp(self, x, W, b, xp):
      # G: group count
      # N: batch size
      # xC: input channels
      # yC: output channels
      G = self.groups
      N, xC = x.shape[:2]
      x_size = x.shape[2:]
      yCg = W.shape[1]
      yC = yCg * G
      xCg = xC // G
      k_size = W.shape[2:]
      dims = len(k_size)
      if xC % G != 0:
          raise TypeError(
              "The number of groups must be "
              "a divisor of that of input channels"
          )
  
      x = xp.rollaxis(x, 1)  # (xC, N, x_size...)
      x = x.reshape(G, xCg, N * utils.size_of_shape(x_size))
  
      W = W.reshape(G, xCg, yCg * utils.size_of_shape(k_size))
      W = W.transpose(0, 2, 1)  # (G, yCg*k_size, xCg)
  
      # (G, yCg*k_size, N*x_size) = (G, yCg*k_size, xCg) @ (G, xCg, N*x_size)
      col = convolution_2d._matmul(W, x).astype(x.dtype, copy=False)
  
      col = col.reshape((yC,) + k_size + (N,) + x_size)
      col = xp.rollaxis(col, dims + 1)  # (N, yC, k_size..., x_size...)
  
      y = conv_nd.col2im_nd(
          col, self.stride, self.pad, self.outs, dilate=self.dilate
      )
  
      if b is not None:
          y += b.reshape(1, yC, *((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.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 5 for calling the DeconvolutionND obj's underlying member method code with example usage: obj._forward_grouped_convolution_xp(x, W, b, xp) and returns: y