code: def _forward_cudnn(self, x, W, b):
      out_c = W.shape[0]  # (c_O, _, k_1, k_2, ..., k_N)
      ksize = W.shape[2:]
      n, c = x.shape[:2]  # (n, c_I, d_1, d_2, ..., d_N)
      dims = x.shape[2:]
      stride = self.stride
      pad = self.pad
      dilate = self.dilate
      groups = self.groups
  
      # Make empty array for result.
      outs = tuple(
          conv.get_conv_outsize(
              d, k, s, p, cover_all=self.cover_all, d=di
          )
          for (d, k, s, p, di) in zip(dims, ksize, stride, pad, dilate)
      )
      assert all(
          out > 0 for out in outs
      ), "Output sizes should be positive."
      y_shape = (n, out_c) + outs  # (n, c_O, out_1, out_2, ..., out_N)
      y = cuda.cupy.empty(y_shape, dtype=x.dtype)
      auto_tune = configuration.config.autotune
      tensor_core = configuration.config.use_cudnn_tensor_core
      cuda.cudnn.convolution_forward(
          x,
          W,
          b,
          y,
          pad,
          stride,
          dilate,
          groups,
          auto_tune=auto_tune,
          tensor_core=tensor_core,
      )
      return (y,)
  
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 8 for calling the ConvolutionND obj's underlying member method code with example usage: obj._forward_cudnn(x, W, b) and returns: y