How to use feature input layer in transfer learning to concatenate the features with the output of fully-connected layer using MATLAB

MathWorks: Train Network on Image and Feature Data

Following the above MATHWORKS example. While performing transfer learning from vgg16, I want to add a concatenation layer with two inputs. One input will be from the last pre-trained layer of the vgg16, which is input 1 (in1) that is true. But, at input 2 (in2) I want to add the hand-crafted features (in mat file), but I cannot make it properly. Please guide me on this. I'm using featureInputLayer to get features and want to use them to concatenate with in1.

close all; clear all; clc;
% Load Pre-trained CNN Model
net = vgg16;
% Replacing the last 3 layers 
TransNet = [ 
    net.Layers(1:end-3)
    %     featureInputLayer(280,'Name','in2')
    concatenationLayer(1,2,'Name','concat')
    fullyConnectedLayer(4096, 'Name','fc8')
    softmaxLayer('Name','sm')
    classificationLayer('Name','classification')];

analyzeNetwork(TransNet);

[Aasim]