code: def add_lrn(self, name, input_name, output_name, alpha, beta, local_size, k = 1.0):
          
  
          spec = self.spec
          nn_spec = self.nn_spec
  
          # Add a new layer
          spec_layer = nn_spec.layers.add()
          spec_layer.name = name
          spec_layer.input.append(input_name)
          spec_layer.output.append(output_name)
  
          spec_layer_params = spec_layer.lrn
          spec_layer_params.alpha = alpha
          spec_layer_params.beta = beta
          spec_layer_params.localSize = local_size
          spec_layer_params.k = k
docs: Add a LRN (local response normalization) layer. Please see the LRNLayerParams message in Core ML neural network
          protobuf for more information about the operation of this layer. Supports "across" channels normalization.
  
          Parameters
          ----------
          name: str
              The name of this layer.
  
          input_name: str
              The input blob name of this layer.
          output_name: str
              The output blob name of this layer.
  
          alpha: float
              multiplicative constant in the denominator.
  
          beta: float
              exponent of the normalizing term in the denominator.
  
          k: float
              bias term in the denominator. Must be positive.
  
          local_size: int
              size of the neighborhood along the channel axis.
  
  
          See Also
          --------
          add_l2_normalize, add_mvn