code: def local_response_normalization(x, n=5, k=2, alpha=1e-4, beta=0.75):
      """Local response normalization across neighboring channels.
  
      This function implements normalization across channels. Let :math:`x` an
      input image with :math:`N` channels. Then, this function computes an output
      image :math:`y` by following formula:
  
      .. math::
         y_i = {x_i \\over \\left( k + \\
                \\alpha \\sum_{j=\\max{1, i - n/2}}^{\\min{N, i + n/2}} \\
                x_j^2 \\right)^\\beta}.
  
      Args:
          x (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable.
          n (int): Normalization window width.
          k (float): Smoothing parameter.
          alpha (float): Normalizer scaling parameter.
          beta (float): Normalizer power parameter.
  
      Returns:
          ~chainer.Variable: Output variable.
  
      See: Section 3.3 of `ImageNet Classification with Deep Convolutional
      Neural Networks <https://www.cs.toronto.edu/~fritz/absps/imagenet.pdf>`_
  
      """
      return LocalResponseNormalization(n, k, alpha, beta).apply((x,))[
          0
      ]
  
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 2 for calling a global function with example usage: local_response_normalization(x, n, k, alpha, beta) and returns: LocalResponseNormalization