code: def _forward_cudnn(self, x, gy):
      # Make empty arrays for result.
      out_c = gy.shape[1]
      in_c = x.shape[1] // self.groups
      gW = cuda.cupy.empty(
          (out_c, in_c) + self.ksize, dtype=self.W_dtype
      )
  
      # Compute
      pad = self.pad
      stride = self.stride
      dilate = self.dilate
      groups = self.groups
      deterministic = configuration.config.cudnn_deterministic
      auto_tune = configuration.config.autotune
      tensor_core = configuration.config.use_cudnn_tensor_core
      cuda.cudnn.convolution_backward_filter(
          x,
          gy,
          gW,
          pad,
          stride,
          dilate,
          groups,
          deterministic=deterministic,
          auto_tune=auto_tune,
          tensor_core=tensor_core,
      )
  
      return (gW,)
  
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 7 for calling the ConvolutionNDGradW obj's underlying member method code with example usage: obj._forward_cudnn(x, gy) and returns: gW