class_docstr: Local Response Normalization Layer.
  
  Aggregation is purely across channels, not within channels,
  and performed "pixelwise".
  
  If the value of the :math:`i` th channel is :math:`x_i`, the output is
  
  .. math::
      x_i = \frac{{x_i}}{{ (k + ( \alpha \sum_j x_j^2 ))^\beta }}
  
  where the summation is performed over this position on :math:`n`
  neighboring channels.
  
  Parameters
  ----------
  alpha : float
      Coefficient, see equation above. Defaults to ``1e-4``.
  
  beta : float
      Offset, see equation above. Defaults to ``0.75``.
  
  k : float
      Exponent, see equation above. Defaults to ``2``.
  
  depth_radius : int
      Number of adjacent channels to normalize over, must be odd.
      Defaults to ``5``.
  
  {Identity.name}
  
  Methods
  -------
  {Identity.Methods}
  
  Attributes
  ----------
  {Identity.Attributes}
  
  Examples
  --------
  >>> from neupy.layers import *
  >>> network = Input((10, 10, 12)) >> LocalResponseNorm()
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code: def output(self, input, **kwargs):
      return tf.nn.local_response_normalization(
          input,
          depth_radius=self.depth_radius,
          bias=self.k,
          alpha=self.alpha,
          beta=self.beta,
      )
  
imports: import tensorflow
  from neupy.core.properties import ProperFractionProperty, ParameterProperty, TypedListProperty, NumberProperty, IntProperty
  from neupy.utils import asfloat
  from neupy.exceptions import WeightInitializationError, LayerConnectionError
  from .base import Identity
label_desc: Use image node_id 3 for calling the LocalResponseNorm obj's underlying member method code with example usage: obj.output(input) and returns: tf