code: def forward_gpu(self, inputs):
      self.retain_inputs((0, 1))  # retain only 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)
  
      n, _, h, w = x_shape
      out_c, _, kh, kw = w_shape
      out_h, out_w = self._get_out_size(x_shape, w_shape)
      y_raw_shape = memory_layouts._transpose_shape(
          (n, out_c, out_h, out_w), None, x_layout
      )
  
      y = cuda.cupy.empty(y_raw_shape, dtype=x.dtype)
  
      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 (self.groups <= 1 or _cudnn_version >= 7000)
      )
  
      if self.cudnn_fast and not use_cudnn:
          raise RuntimeError("'cudnn_fast' requires cuDNN to work")
  
      if use_cudnn:
          # cuDNN implementation
          return self._forward_cudnn(x, W, b, y, (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 import backend
  from chainer.backends import cuda
  from chainer.backends import intel64
  from chainer import configuration
  from chainer import function_node
  import chainer.functions
  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 10 for calling the Convolution2DFunction obj's underlying member method code with example usage: obj.forward_gpu(inputs) and returns: self, self, self