code: def forward_chainerx(self, inputs):
      # TODO(hvy): Support mixed precision.
      if any([arr.dtype != inputs[0].dtype for arr in inputs[1:]]):
          return chainer.Fallback
      # TODO(hvy): Support dilate > 1.
      if any(d != 1 for d in self.dilate):
          return chainer.Fallback
      # TODO(hvy): Support groups > 1.
      if self.groups > 1:
          return chainer.Fallback
      if inputs[0].device.backend.name == "cuda" and (
          self.cover_all or self.ndim < 2
      ):
          return chainer.Fallback
  
      return (
          chainerx.conv(
              *inputs,
              stride=self.stride,
              pad=self.pad,
              cover_all=self.cover_all,
          ),
      )
  
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 ConvolutionND obj's underlying member method code with example usage: obj.forward_chainerx(inputs) and returns: chainerx, chainer, chainer, chainer, chainer