code: def forward(self, inputs):
      self.retain_inputs((0, 1))
      x, gy = inputs
  
      xp = backend.get_array_module(*inputs)
      if xp is numpy:
          return self._forward_xp(x, gy, numpy)
      elif not self._use_cudnn(x, gy):
          return self._forward_xp(x, gy, cuda.cupy)
      else:
          return self._forward_cudnn(x, gy)
  
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 3 for calling the ConvolutionNDGradW obj's underlying member method code with example usage: obj.forward(inputs) and returns: self, self, self