Incorrect number of output for custom layer

Hi, i try to create a custom layer than dulipcate the feature vector multiple times according to input arguement. I only define for predict becuase i saw the documentation saying that if the predict and forward is same, predict will be used for training. But i got the error for both forward and predict which i dont understand what is happening. May i know what is happening here, thank you

classdef repeatVectorLayer < nnet.layer.Layer % & nnet.layer.Formattable (Optional)

    properties
        % (Optional) Layer properties.
        repeated_timestep=0;
        % Declare layer properties here.
    end

    properties (Learnable)
        % (Optional) Layer learnable parameters.

        % Declare learnable parameters here.
    end

    properties (State)
        % (Optional) Layer state parameters.

        % Declare state parameters here.
    end

    properties (Learnable, State)
        % (Optional) Nested dlnetwork objects with both learnable
        % parameters and state.

        % Declare nested networks with learnable and state parameters here.
    end

    methods
        function layer = repeatVectorLayer(n)
            % (Optional) Create a myLayer.
            % This function must have the same name as the class.
            layer.repeated_timestep=n;
            % Define layer constructor function here.
        end

        function Z = predict(layer,X)
            % Forward input data through the layer at prediction time and
            % output the result and updated state.
            %
            % Inputs:
            %         layer - Layer to forward propagate through 
            %         X     - Input data
            % Outputs:
            %         Z     - Output of layer forward function
            %         state - (Optional) Updated layer state.
            %
            %  - For layers with multiple inputs, replace X with X1,...,XN, 
            %    where N is the number of inputs.
            %  - For layers with multiple outputs, replace Z with 
            %    Z1,...,ZM, where M is the number of outputs.
            %  - For layers with multiple state parameters, replace state 
            %    with state1,...,stateK, where K is the number of state 
            %    parameters.

            % Define layer predict function here.
            Z=repmat(X,[layer.repeated_timestep,1]); 
        end

        function [Z,state,memory] = forward(layer,X)
            % (Optional) Forward input data through the layer at training
            % time and output the result, updated state, and a memory
            % value.
            %
            % Inputs:
            %         layer - Layer to forward propagate through 
            %         X     - Layer input data
            % Outputs:
            %         Z      - Output of layer forward function 
            %         state  - (Optional) Updated layer state 
            %         memory - (Optional) Memory value for custom backward
            %                  function
            %
            %  - For layers with multiple inputs, replace X with X1,...,XN, 
            %    where N is the number of inputs.
            %  - For layers with multiple outputs, replace Z with 
            %    Z1,...,ZM, where M is the number of outputs.
            %  - For layers with multiple state parameters, replace state 
            %    with state1,...,stateK, where K is the number of state 
            %    parameters.

            % Define layer forward function here.
        end
 -----------------------------------------------------------------------------------------------------------------   
        Caused by:
    Layer 3: Layer validation failed. Incorrect number of output arguments for 'forward'. Expected to have 2, but instead it has 3.
    Layer 3: Error using 'forward' in layer repeatVectorLayer. The function threw an error and could not be executed.
        Output argument "Z" (and possibly others) not assigned a value in the execution with "repeatVectorLayer/forward" function.