code: def _forward_cudnn(self, x, W, b, input_layouts):
      x_layout, w_layout = input_layouts
      self.output_layouts = (x_layout,)
      n = len(x)
  
      _, c, _, _ = memory_layouts._transpose_shape(
          W.shape, w_layout, None
      )
      y_raw_shape = memory_layouts._transpose_shape(
          (n, c * self.groups, self.outh, self.outw), None, x_layout
      )
  
      y = cuda.cupy.empty(y_raw_shape, dtype=x.dtype)
      pad = (self.ph, self.pw)
      stride = (self.sy, self.sx)
      dilation = (self.dy, self.dx)
      deterministic = configuration.config.cudnn_deterministic
      auto_tune = configuration.config.autotune
      tensor_core = configuration.config.use_cudnn_tensor_core
      cudnn_x_layout = cuda._get_cudnn_tensor_layout_x(x_layout)
      cudnn_w_layout = cuda._get_cudnn_tensor_layout_w(w_layout)
      cuda.cudnn.convolution_backward_data(
          W,
          x,
          b,
          y,
          pad,
          stride,
          dilation,
          self.groups,
          deterministic=deterministic,
          auto_tune=auto_tune,
          tensor_core=tensor_core,
          d_layout=cudnn_x_layout,
          w_layout=cudnn_w_layout,
      )
  
      return (y,)
  
imports: import numpy
  import chainer
  from chainer.backends import cuda
  from chainer.backends import intel64
  from chainer import configuration
  from chainer import function_node
  import chainer.functions
  from chainer.functions.connection import convolution_2d
  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 11 for calling the Deconvolution2DFunction obj's underlying member method code with example usage: obj._forward_cudnn(x, W, b, input_layouts) and returns: y