Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter $.$ You are required to calculate the

RBF function and search yourself, instead of using functions in Scikit$-$Learn $($sklearn$).$

def parameter$\_$search$($gamma$\_$list,Xtr$,$ytr$,$Xts$,$yts$,..)$ :

$"""$

Parameters

gamma$\_$list: A list of RBF kernel parameter gamma to search for

Xtr: Training data, shape $($ntr$,$nrow,ncol$)$

ytr: Training labels, shape $($ntr$,)$

Xts: Test data, shape $($nts$,$ nrow,ncol$)$

yts: Test labels, shape $($nts$,)$

$...$ Add other parameters as needed

Returns :

gamma$\_$optimum: The gamma with minimum error on Xts$,$ yts

$"""$

$..$

return gamma$\_$optimum