code: def forward_cpu(self, inputs):
      if self.cudnn_fast:
          raise RuntimeError(
              "'cudnn_fast' can't be used in the CPU backend"
          )
  
      self._check_input_layouts_all_standard()
      self.retain_inputs((0, 1))  # retain only x and W
      if len(inputs) == 2:
          (x, W), b = inputs, None
      else:
          x, W, b = inputs
  
      if intel64.should_use_ideep(
          ">=auto"
      ) and intel64.inputs_all_ready(inputs):
          self._use_ideep = True
  
      if self.groups > 1:
          return self._forward_grouped_convolution(x, W, b)
      else:
          return self._forward_cpu_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 7 for calling the Convolution2DFunction obj's underlying member method code with example usage: obj.forward_cpu(inputs) and returns: self, self