class_docstr: Cross-channel normalization function used in AlexNet.
code: def forward_cpu(self, inputs):
      if intel64.should_use_ideep(
          ">=auto"
      ) and intel64.inputs_all_ready(inputs, (4,)):
          self._use_ideep = True
          return self.forward_ideep(inputs)
  
      (x,) = inputs
      self.retain_inputs((0,))
      self.retain_outputs((0,))
  
      half_n = self.n // 2
      x2 = numpy.square(x)
      sum_part = x2.copy()
      for i in six.moves.range(1, half_n + 1):
          sum_part[:, i:] += x2[:, :-i]
          sum_part[:, :-i] += x2[:, i:]
      self.unit_scale = self.k + self.alpha * sum_part
      self.scale = self.unit_scale**-self.beta
      y = x * self.scale
      return (y,)
  
imports: import numpy
  import six
  from chainer.backends import cuda
  from chainer.backends import intel64
  from chainer import function_node
  from chainer.utils import type_check
label_desc: Use image node_id 3 for calling the LocalResponseNormalization obj's underlying member method code with example usage: obj.forward_cpu(inputs) and returns: y, self