algorithm stochastic gradient descent with reshuffling inputtraining set tn {(xi yi )}ni= initial weight matrices and bias vectors {wl bl } = th activation functions {sl } = learning rates { outputthe parameters of the trained learner and epoch while stopping condition is not met do iid draw un ( let be the permutation of { nthat satisfies up upn (xi yi (xpi ypi for /reshuffle tn for do jn ` = - ) + loss(yi (xi th) tht+ tht at th` (tht - + epoch epoch /number of reshuffles or epochs return tht as the minimizer of the training loss write python code that implements the stochastic gradient descent with data reshufflingand use it to train the neural net in section denote the pdf of the ( sdistribution by phs (*)and let phs ( ( phs ( ln dx phs ( rd be the kullback-leibler divergence between the densities of the ( and ( distributions on rd show that - - ( tr( - ln | ( ( hencededuce the formula in ( suppose that we wish to compute the inverse and log-determinant of the matrix in uuwhere is an matrix with show that (in uu)- in qn > where qn contains the first rows of the ( hx matrix in the qr factorization of the ( hx matrixu qr ih in additionshow that ln |in uuhi= ln rii where {rii are the diagonal elements of the matrix