code: def _forward_grouped_convolution_xp(self, x, gy, xp):
      G = self.groups
      N, iC = x.shape[:2]
      oC = gy.shape[1]
      o_size = gy.shape[2:]
      o_size_prod = utils.size_of_shape(o_size)
      k_size = self.ksize
      dims = len(o_size)
      iCg = iC // G
      oCg = oC // G
  
      # Do not check iCg and oCg because this class is rarely used alone
  
      # (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,
      )
  
      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 * o_size_prod)
      x = x.transpose(0, 2, 1)  # (G, N*o_size, iCg*k_size)
  
      gy = xp.rollaxis(gy, 1)  # (oC, N, o_size...)
      gy = gy.reshape(G, oCg, N * o_size_prod)
  
      # (G, oCg, iCg*k_size) = (G, oCg, N*o_size) @ (G, N*o_size, iCg*k_size)
      gW = convolution_2d._matmul(gy, x).astype(
          self.W_dtype, copy=False
      )
      gW = gW.reshape(oC, iCg, *k_size)
  
      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 5 for calling the ConvolutionNDGradW obj's underlying member method code with example usage: obj._forward_grouped_convolution_xp(x, gy, xp) and returns: gW