code: def _forward_grouped_convolution(self, x, gy):
      # G: group count
      # N: batch size
      # kH, kW: kernel height, kernel width
      # iC, iH, iW: input channels, input height, input width
      # oC, oH, oW: output channels, output height, output width
      G = self.groups
      N, iC, iH, iW = x.shape
      _, oC, oH, oW = gy.shape  # _ == N
      kH = self.kh
      kW = self.kw
      iCg = iC // G
      oCg = oC // G
  
      # (N, iC, kH, kW, oH, oW)
      x = conv.im2col(
          x,
          kH,
          kW,
          self.sy,
          self.sx,
          self.ph,
          self.pw,
          cover_all=self.cover_all,
          dy=self.dy,
          dx=self.dx,
      )
  
      x = x.transpose(1, 2, 3, 0, 4, 5)  # (iC, kH, kW, N, oH, oW)
      x = x.reshape(G, iCg * kH * kW, N * oH * oW)
      x = x.transpose(0, 2, 1)  # (G, N*oH*oW, iCg*kH*kW)
  
      gy = gy.transpose(1, 0, 2, 3)  # (oC, N, oH, oW)
      gy = gy.reshape(G, oCg, N * oH * oW)
  
      # (G, oCg, iCg*kH*kW) = (G, oCg, N*oH*oW) @ (G, N*oH*oW, iCg*kH*kW)
      gW = _matmul(gy, x).astype(self.W_dtype, copy=False)
      gW = gW.reshape(oC, iCg, kH, kW)
  
      return (gW,)
  
imports: import numpy
  import chainer
  from chainer import backend
  from chainer.backends import cuda
  from chainer.backends import intel64
  from chainer import configuration
  from chainer import function_node
  import chainer.functions
  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 8 for calling the Convolution2DGradW obj's underlying member method code with example usage: obj._forward_grouped_convolution(x, gy) and returns: gW