Define custom fully connected layer

Hi. I am trying to define a custom fully connected layer after some convolution layers.

classdef fully < nnet.layer.Layer

    properties
        % (Optional) Layer properties

        % Layer properties go here

    end

    properties (Learnable)
        % (Optional) Layer learnable parameters

        % Layer learnable parameters go here
        W;
        b;
    end

    methods
        function layer = fully(input_shape, output_shape, name)
            % (Optional) Create a myLayer
            % This function must have the same name as the layer

            % Layer constructor function goes here
            layer.Name = name;
            layer.Description = 'fully self';
            layer.b = rand([1 output_shape]);
            layer.W = rand([input_shape, output_shape]);
        end

        function Z = predict(layer, X)
            % Forward input data through the layer at prediction time and
            % output the result
            %
            % Inputs:
            %         layer    -    Layer to forward propagate through
            %         X        -    Input data
            % Output:
            %         Z        -    Output of layer forward function

            % Layer forward function for prediction goes here
            disp('W')
            size(layer.W)
            disp('X')
            size(X)
            Z = X*layer.W + layer.b;
        end
% 
%         function [Z, memory] = forward(layer, X)
%             % (Optional) Forward input data through the layer at training
%             % time and output the result and a memory value
%             %
%             % Inputs:
%             %         layer  - Layer to forward propagate through
%             %         X      - Input data
%             % Output:
%             %         Z      - Output of layer forward function
%             %         memory - Memory value which can be used for
%             %                  backward propagation
% 
%             % Layer forward function for training goes here
%             Z = layer.W*X + layer.b;
%             memory.W = layer.W;
%             memory.b = layer.b;
%         end

        function [dLdX, dLdW, dLdB] = backward(layer, X, Z, dLdZ, memory)
            % Backward propagate the derivative of the loss function through 
            % the layer
            %
            % Inputs:
            %         layer             - Layer to backward propagate through
            %         X                 - Input data
            %         Z                 - Output of layer forward function            
            %         dLdZ              - Gradient propagated from the deeper layer
            %         memory            - Memory value which can be used in
            %                             backward propagation
            % Output:
            %         dLdX              - Derivative of the loss with respect to the
            %                             input data
            %         dLdW1, ..., dLdWn - Derivatives of the loss with respect to each
            %                             learnable parameter

            % Layer backward function goes here
            dLdX
        end
    end
end

My question is how could this library parse the input shape of the previous layer. I mean the previous convolution layer so that I could initialize the parameter for W? Thank you so much.