partial tucker decomposition

I want to apply a partial tucker decomposition algorithm to minimize MNIST image tensor dataset of (60000,28,28), in order to conserve its features when applying another machine algorithm afterwards like SVM. I have this code that minimizes the second and third dimension of the tensor

i = 16
j = 10
core, factors = partial_tucker(train_data_mnist, modes=[1,2],tol=10e-5, rank=[i,j])
train_datapartial_tucker = tl.tenalg.multi_mode_dot(train_data_mnist, factors, 
                              modes=modes, transpose=True)
test_data_partial_tucker = tl.tenalg.multi_mode_dot(test_data_mnist, factors, 
                              modes=modes, transpose=True)

How to find the best rank [i,j] when I'm using partial_tucker in tensorly that will give the best dimension reduction for the image while conserving as much data?

from partial tucker decomposition