code: def forward_gpu(self, inputs):
      self.retain_inputs((0, 1))  # only retain x and W
      if len(inputs) == 2:
          (x, W), b = inputs, None
          x_layout, w_layout = self.input_layouts
      else:
          x, W, b = inputs
          x_layout, w_layout, _ = self.input_layouts
  
      x_shape = memory_layouts._transpose_shape(x.shape, x_layout, None)
      w_shape = memory_layouts._transpose_shape(W.shape, w_layout, None)
      self._calc_out_size(x_shape, w_shape)
      self._set_cover_all(x_shape, w_shape)
  
      use_cudnn = (
          chainer.should_use_cudnn(">=auto")
          and not self.cover_all
          and x.dtype == W.dtype
          and (
              (self.dy == 1 and self.dx == 1)
              or (
                  _cudnn_version >= 6000
                  and not configuration.config.cudnn_deterministic
              )
          )
          and (self.groups <= 1 or _cudnn_version >= 7000)
      )
  
      if use_cudnn:
          # cuDNN implementation
          return self._forward_cudnn(x, W, b, (x_layout, w_layout))
  
      elif self.groups > 1:
          return self._forward_grouped_convolution(x, W, b)
  
      else:
          return self._forward_gpu_core(x, W, b)
  
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 8 for calling the Deconvolution2DFunction obj's underlying member method code with example usage: obj.forward_gpu(inputs) and returns: self, self, self