def add_lrn(self, name, input_name, output_name, alpha, beta, local_size, k = 1.0):
        """
        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
        """

        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
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