code: def _forward_grouped_convolution(self, x, W, b):
      # 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, _, kH, kW = W.shape  # _ == iCg
      iCg = iC // G
      oCg = oC // G
  
      # (N, iC, kW, 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,
      )
      oH, oW = x.shape[-2:]
  
      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)
  
      W = W.reshape(G, oCg, iCg * kH * kW)
  
      # (G, oCg, N*oH*oW) = (G, oCg, iCg*kH*kW) @ (G, iCg*kH*kW, N*oH*oW)
      y = _matmul(W, x).astype(x.dtype, copy=False)
      y = y.reshape(oC, N, oH, oW)
      y = y.transpose(1, 0, 2, 3)  # (N, oC, oH, oW)
      if b is not None:
          y += b.reshape(1, b.size, 1, 1)
  
      return (y,)
  
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 12 for calling the Convolution2DFunction obj's underlying member method code with example usage: obj._forward_grouped_convolution(x, W, b) and returns: y