Undefined function or variable 'projectAndReshapeLayer' in matlab 2018a - matlab

Question

I am implementing CGAN in https://www.mathworks.com/help/deeplearning/ug/train-conditional-generative-adversarial-network.html
and when i run it i am getting this error: Undefined function or variable 'projectAndReshapeLayer'
I searched and found out that i need to make a file called projectAndReshapeLayer.m and i got the code from matalb site and made it but when i am running it i get this error:
Abstract classes cannot be instantiated. Class 'projectAndReshapeLayer' inherits abstract methods or properties but does not implement
them. See the list of methods and properties that 'projectAndReshapeLayer' must implement if you do not intend the class to be abstract.
when i click "See the list of methods and properties" I get:
Abstract methods for class projectAndReshapeLayer:
backward % defined in nnet.layer.Layer
when i searched i understood that i need a backward function in the projectAndReshapeLayer.m code that I added but I cant find it can anyone help?
this is the projectAndReshapeLayer.m file:
classdef projectAndReshapeLayer < nnet.layer.Layer
properties
% (Optional) Layer properties.
OutputSize
end
properties (Learnable)
% Layer learnable parameters.
Weights
Bias
end
methods
function layer = projectAndReshapeLayer(outputSize, numChannels, name)
% Create a projectAndReshapeLayer.
% Set layer name.
layer.Name = name;
% Set layer description.
layer.Description = "Project and reshape layer with output size " + join(string(outputSize));
% Set layer type.
layer.Type = "Project and Reshape";
% Set output size.
layer.OutputSize = outputSize;
% Initialize fully connect weights and bias.
fcSize = prod(outputSize);
layer.Weights = initializeGlorot(fcSize, numChannels);
layer.Bias = zeros(fcSize, 1, 'single');
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, specified as a 1-by-1-by-C-by-N
% dlarray, where N is the mini-batch size.
% Outputs:
% Z - Output of layer forward function returned as
% an sz(1)-by-sz(2)-by-sz(3)-by-N dlarray,
% where sz is the layer output size and N is
% the mini-batch size.
% Fully connect.
weights = layer.Weights;
bias = layer.Bias;
X = fullyconnect(X,weights,bias,'DataFormat','SSCB');
% Reshape.
outputSize = layer.OutputSize;
Z = reshape(X, outputSize(1), outputSize(2), outputSize(3), []);
end
end
end
function weights = initializeGlorot(numOut, numIn)
% Initialize weights using uniform Glorot.
varWeights = sqrt( 6 / (numIn + numOut) );
weights = varWeights * (2 * rand([numOut, numIn], 'single') - 1);
end