Does "Define Custom Classification Output Layer" not support adding learning parameters?

I want to customize a cross-entropy classification loss function. In this loss, I want to add parameters that can be learned, but after I write it out, I don’t support adding it. How can I solve it?

unknow attribute name "Learnable"???

Is it so inflexible? I hope that future versions will greatly support the freedom and flexibility of "Define Custom Layer" in many aspects! ! !

classdef myClassificationLayer < nnet.layer.ClassificationLayer
        
    properties (Learnable) % unknow attribute name "Learnable"???
        % Layer learnable parameters
            
        % kernel parameter ,size: embedding_size*classnum, 每列代表一个“代理特征向量”
        kernel 
    end
    properties
        % (Optional) Layer properties.

        % Layer properties go here.
    end
 
    methods
        function layer = myClassificationLayer()           
            % (Optional) Create a myClassificationLayer.
             ...
            % Layer constructor function goes here.
        end

        function loss = forwardLoss(layer, Y, T)
            % Return the loss between the predictions Y and the training 
            % targets T.
            %
            % Inputs:
            %         layer - Output layer
            %         Y     – Predictions made by network
            %         T     – Training targets
            %
            % Output:
            %         loss  - Loss between Y and T

            ...
                
            % Layer forward loss function goes here.
        end
        
        function dLdY = backwardLoss(layer, Y, T)
            % (Optional) Backward propagate the derivative of the loss 
            % function.
            %
            % Inputs:
            %         layer - Output layer
            %         Y     – Predictions made by network
            %         T     – Training targets
            %
            % Output:
            %         dLdY  - Derivative of the loss with respect to the 
            %                 predictions Y
            ...
                
            % Layer backward loss function goes here.
        end
    end
end