Understanding LSTM Sequence to Sequence Mathworks example

Hi, in this example for a Sequence to Sequence regression LSTM there's a call to a function named "prepareDataTrain"

here's the function:

function [XTrain,YTrain] = prepareDataTrain(filenamePredictors)

dataTrain = dlmread(filenamePredictors);

numObservations = max(dataTrain(:,1));

XTrain = cell(numObservations,1);
YTrain = cell(numObservations,1);
for i = 1:numObservations
    idx = dataTrain(:,1) == i;
    
    X = dataTrain(idx,3:end)';
    XTrain{i} = X;
    
    timeSteps = dataTrain(idx,2)';
    Y = fliplr(timeSteps);
    YTrain{i} = Y;
end

end

the dataTrain table looks like this:

1 1 -0.0007 -0.0004 100.0 518.67 641.82 1589.70 1400.60 14.62 21.61 554.36 2388.06 9046.19 1.30 47.47 521.66 2388.02 8138.62 8.4195 0.03 392 2388 100.00 39.06

1 2 0.0019 -0.0003 100.0 518.67 642.15 1591.82 1403.14 14.62 21.61 553.75 2388.04 9044.07 1.30 47.49 522.28 2388.07 8131.49 8.4318 0.03 392 2388 100.00 39.00

1 3 -0.0043 0.0003 100.0 518.67 642.35 1587.99 1404.20 14.62 21.61 554.26 2388.08 9052.94 1.30 47.27 522.42 2388.03 8133.23 8.4178 0.03 390 2388 100.00 38.95

1 4 0.0007 0.0000 100.0 518.67 642.35 1582.79 1401.87 14.62 21.61 554.45 2388.11 9049.48 1.30 47.13 522.86 2388.08 8133.83 8.3682 0.03 392 2388 100.00 38.88

1 5 -0.0019 -0.0002 100.0 518.67 642.37 1582.85 1406.22 14.62 21.61 554.00 2388.06 9055.15 1.30 47.28 522.19 2388.04 8133.80 8.4294 0.03 393 2388 100.00 38.90

1 6 -0.0043 -0.0001 100.0 518.67 642.10 1584.47 1398.37 14.62 21.61 554.67 2388.02 9049.68 1.30 47.16 521.68 2388.03 8132.85 8.4108 0.03 391 2388 100.00 38.98

1 7 0.0010 0.0001 100.0 518.67 642.48 1592.32 1397.77 14.62 21.61 554.34 2388.02 9059.13 1.30 47.36 522.32 2388.03 8132.32 8.3974 0.03 392 2388 100.00 39.10

1 8 -0.0034 0.0003 100.0 518.67 642.56 1582.96 1400.97 14.62 21.61 553.85 2388.00 9040.80 1.30 47.24 522.47 2388.03 8131.07 8.4076 0.03 391 2388 100.00 38.97

1 9 0.0008 0.0001 100.0 518.67 642.12 1590.98 1394.80 14.62 21.61 553.69 2388.05 9046.46 1.30 47.29 521.79 2388.05 8125.69 8.3728 0.03 392 2388 100.00 39.05

What I don't understand in this function is why is flipping the timeSteps when they are correctly ordered. Could someone please explain this to me?

Thank you for your time.