code: def _forward_grouped_convolution(self, x, W, b):
      # G: group count
      # N: batch size
      # kH, kW: kernel height, kernel width
      # xC, xH, xW: x channels, x height, x width
      # yC, yH, yW: y channels, y height, y width
      G = self.groups
      N, xC, xH, xW = x.shape
      xCg = xC // G
      _, yCg, kH, kW = W.shape  # _ == xC
      yC = yCg * G
  
      x = x.transpose(1, 0, 2, 3)  # (xC, N, xH, xW)
      x = x.reshape(G, xCg, N * xH * xW)
  
      W = W.reshape(G, xCg, yCg * kH * kW)
      W = W.transpose(0, 2, 1)  # (G, yCg*kH*kW, xCg)
  
      # (G, yCg*kH*kW, N*xH*xW) = (G, yCg*kH*kW, xCg) @ (G, xCg, N*xH*xW)
      col = convolution_2d._matmul(W, x).astype(x.dtype, copy=False)
  
      col = col.reshape(yC, kH, kW, N, xH, xW)
      col = col.transpose(3, 0, 1, 2, 4, 5)  # (N, yC, kH, kW, xH, xW)
  
      y = conv.col2im(
          col,
          self.sy,
          self.sx,
          self.ph,
          self.pw,
          self.outh,
          self.outw,
          dy=self.dy,
          dx=self.dx,
      )
  
      if b is not None:
          y += b.reshape(1, b.size, 1, 1)
      return (y,)
  
imports: import numpy
  import chainer
  from chainer.backends import cuda
  from chainer.backends import intel64
  from chainer import configuration
  from chainer import function_node
  import chainer.functions
  from chainer.functions.connection import convolution_2d
  from chainer import memory_layouts
  from chainer.utils import argument
  from chainer.utils import conv
  from chainer.utils import type_check
  import chainerx
label_desc: Use image node_id 10 for calling the Deconvolution2DFunction obj's underlying member method code with example usage: obj._forward_grouped_convolution(x, W, b) and returns: y