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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code: def __init__(
      self,
      depth_radius=None,
      bias=None,
      alpha=None,
      beta=None,
      name=None,  #'lrn',
  ):
      # super(LocalResponseNorm, self).__init__(prev_layer=prev_layer, name=name)
      super().__init__(name)
      self.depth_radius = depth_radius
      self.bias = bias
      self.alpha = alpha
      self.beta = beta
  
      logging.info(
          "LocalResponseNorm %s: depth_radius: %s, bias: %s, alpha: %s, beta: %s"
          % (
              self.name,
              str(depth_radius),
              str(bias),
              str(alpha),
              str(beta),
          )
      )
  
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 1 to create a new LocalResponseNorm object from inherited base classes: Layer with example: obj = LocalResponseNorm(depth_radius, bias, alpha, beta, name)