code: def forward_gpu(self, inputs):
      self.retain_inputs((0, 1))
      x, gy = inputs
  
      use_cudnn = (
          chainer.should_use_cudnn(">=auto")
          and not self.cover_all
          and x.dtype == self.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, gy)
  
      elif self.groups > 1:
          return self._forward_grouped_convolution(x, gy)
  
      else:
          return self._forward_gpu_core(x, gy)
  
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 6 for calling the Convolution2DGradW obj's underlying member method code with example usage: obj.forward_gpu(inputs) and returns: self, self, self