What is the difference between using cvpartition then calling it to kfoldLoss and using it directly in kfoldLoss?

Hello,

I am based on the following link (https://fr.mathworks.com/help/stats/bayesopt.html) to produce the following code

clear all load ionosphere

%variables to optimize

box = optimizableVariable('box',[1e-4,1e3],'Transform','log');

kern = optimizableVariable('kern',[1e-4,1e3],'Transform','log');

s = rng % for reproductibility, Obtain the current state of the random stream

%%%%%%%%%%%%%%%%%%%%%%% FIRST PART%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%cross-validation

c = cvpartition(351,'Kfold',5);%351=number of examples in ionosphere

%function to minimize: cross-validation loss

vars = [box,kern];

fun = @(vars)kfoldLoss(fitcsvm(X,Y,'CVPartition',c,'BoxConstraint',vars.box,'KernelScale',vars.kern));

%optimization:Search for the parameters that give the lowest cross-validation error by using parallel Bayesian optimization.

results = bayesopt(fun,vars,'AcquisitionFunctionName','expected-improvement-plus');%results = bayesopt(fun,vars,'UseParallel',true,'AcquisitionFunctionName','expected-improvement-plus');

%Return the best feasible point in the Bayesian model results by using the bestPoint function.

%Use the default criterion min-visited-upper-confidence-interval, which determines the best feasible point as the visited point that minimizes an upper confidence interval on the objective function value.

zbest = bestPoint(results)

%%%%%%%%%%%%%%%%%%%%%%%% SECOND PART%%%%%%%%%%%%%%%%%%%%%%%%%%%%

rng(s) % for reproductibility, Reset the stream to the previous state

fun2 = @(vars)kfoldLoss(fitcsvm(X,Y,'BoxConstraint',vars.box,'KernelScale',vars.kern,...

'Kfold',5));

results = bayesopt(fun2,vars);%,'AcquisitionFunctionName','expected-improvement-plus');

zbest2 = bestPoint(results)

I am wondering why I don't find the same results in the first and second part: in the first part, I defined the cvpartition and I called in fun. In the second part, I defined the cvpartition in the fun2.

Thank you in advance