code: def local_response_normalization(
      incoming,
      depth_radius=5,
      bias=1.0,
      alpha=0.0001,
      beta=0.75,
      name="LocalResponseNormalization",
  ):
      """Local Response Normalization.
  
      Input:
          4-D Tensor Layer.
  
      Output:
          4-D Tensor Layer. (Same dimension as input).
  
      Arguments:
          incoming: `Tensor`. Incoming Tensor.
          depth_radius: `int`. 0-D.  Half-width of the 1-D normalization window.
              Defaults to 5.
          bias: `float`. An offset (usually positive to avoid dividing by 0).
              Defaults to 1.0.
          alpha: `float`. A scale factor, usually positive. Defaults to 0.0001.
          beta: `float`. An exponent. Defaults to `0.5`.
          name: `str`. A name for this layer (optional).
  
      """
  
      with tf.name_scope(name) as scope:
          inference = tf.nn.lrn(
              incoming,
              depth_radius=depth_radius,
              bias=bias,
              alpha=alpha,
              beta=beta,
              name=name,
          )
  
      inference.scope = scope
  
      # Track output tensor.
      tf.add_to_collection(
          tf.GraphKeys.LAYER_TENSOR + "/" + name, inference
      )
  
      return inference
  
file_path: tflearn/tflearn/layers/normalization.py
imports: from __future__ import division, print_function, absolute_import
  import tensorflow.compat.v1
  from tensorflow.python.training import moving_averages
  import tflearn
  from ..None import utils
  from ..None import variables
  from ..utils import get_from_module
label_desc: Use image node_id 3 for calling a global function with example usage: local_response_normalization(incoming, depth_radius, bias, alpha, beta, name) and returns: inference