code: def check_layout_forward(self, inputs):
      input_layouts = self.input_layouts
      n = len(inputs)
  
      layouts = (
          (
              memory_layouts.CUDNN_CHANNEL_FIRST_X,
              memory_layouts.CUDNN_CHANNEL_LAST_X,
          ),
          (
              memory_layouts.CUDNN_CHANNEL_FIRST_W,
              memory_layouts.CUDNN_CHANNEL_LAST_W,
          ),
          (None,),
      )
      for i, (input_layout, expected_layouts) in enumerate(
          zip(input_layouts, layouts[:n])
      ):
          if input_layout not in expected_layouts:
              raise RuntimeError(
                  "Invalid layout for input {}: {}".format(
                      i, input_layout
                  )
              )
  
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 3 for calling the Convolution2DFunction obj's underlying member method code with example usage: obj.check_layout_forward(inputs) without return types