code: def forward_cpu(self, inputs):
      if (
          (self.dy == 1 and self.dx == 1)
          and intel64.should_use_ideep(">=auto")
          and intel64.inputs_all_ready(inputs)
      ):
          self._use_ideep = True
  
      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)
  
      if self.groups > 1:
          # Grouped convolution implementation
          return self._forward_grouped_convolution(x, W, b)
  
      elif intel64.should_use_ideep(
          ">=auto"
      ) and intel64.inputs_all_ready(inputs):
          # iDeep implementation
          self._use_ideep = True
          return self._forward_ideep(x, W, b)
  
      else:
          return self._forward_cpu_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 5 for calling the Deconvolution2DFunction obj's underlying member method code with example usage: obj.forward_cpu(inputs) and returns: self, self, self