code: def _forward_ideep(self, x, W, b):
      out_c, input_c, kh, kw = W.shape
      n, c, h, w = x.shape
  
      out_h, out_w = self._get_out_size(x.shape, W.shape)
      pd = (
          self.sy * (out_h - 1)
          + (kh + (kh - 1) * (self.dy - 1))
          - h
          - self.ph
      )
      pr = (
          self.sx * (out_w - 1)
          + (kw + (kw - 1) * (self.dx - 1))
          - w
          - self.pw
      )
      param = intel64.ideep.convolution2DParam(
          (n, out_c, out_h, out_w),
          self.dy,
          self.dx,
          self.sy,
          self.sx,
          self.ph,
          self.pw,
          pd,
          pr,
      )
      y = intel64.ideep.convolution2D.Forward(
          intel64.ideep.array(x),
          intel64.ideep.array(W),
          intel64.ideep.array(b) if b is not None else None,
          param,
      )
      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 9 for calling the Convolution2DFunction obj's underlying member method code with example usage: obj._forward_ideep(x, W, b) and returns: y