class_docstr: The :class:`LocalResponseNorm` layer is for Local Response Normalization.
  See ``tf.nn.local_response_normalization`` or ``tf.nn.lrn`` for new TF version.
  The 4-D input tensor is a 3-D array of 1-D vectors (along the last dimension), and each vector is normalized independently.
  Within a given vector, each component is divided by the weighted square-sum of inputs within depth_radius.
  
  Parameters
  -----------
  depth_radius : int
      Depth radius. 0-D. Half-width of the 1-D normalization window.
  bias : float
      An offset which is usually positive and shall avoid dividing by 0.
  alpha : float
      A scale factor which is usually positive.
  beta : float
      An exponent.
  name : None or str
      A unique layer name.
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docstr: prev_layer : :class:`Layer`
      The previous layer with a 4D output shape.
docstr_tok: ["prev_layer",":",":","class",":","`","Layer","`","The","previous","layer","with","a","4D","output","shape","."]
code: def forward(self, inputs):
      """
      prev_layer : :class:`Layer`
          The previous layer with a 4D output shape.
      """
      outputs = tf.nn.lrn(
          inputs,
          depth_radius=self.depth_radius,
          bias=self.bias,
          alpha=self.alpha,
          beta=self.beta,
      )
      return outputs
  
imports: import tensorflow
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.training import moving_averages
  import tensorlayer
  from tensorlayer import logging
  from tensorlayer.layers.core import Layer
label_desc: Use image node_id 3 for calling the LocalResponseNorm obj's underlying member method code with example usage: obj.forward(inputs) and returns: outputs