Computational speed for PyTorch tensors using cuda().squeeze() and squeeze().cuda()

I am wondering which of the following codes in PyTorch work faster:

my_tensor.cuda().squeeze() vs. my_tensor.squeeze().cuda()

Check with the code:

time_start_vocoder = time.perf_counter()          

a = my_tensor.cuda().squeeze()

            

time_elapsed_test = (time.perf_counter() - time_start_vocoder)

print('a time=',time_elapsed_test)

            

time_start_vocoder = time.perf_counter()

            

b = my_tensor.squeeze().cuda()

            

print('b time=',time_elapsed_test)

Where

my_tensor.shape = torch.Size([1, a_large_number])

Result:

Run 1:

a time= 0.00013689976185560226

b time= 9.429734200239182e-05

Run 2:

a time= 0.0001775706186890602

b time= 0.00010665040463209152

Run 3:

a time= 0.0001584356650710106

b time= 6.485264748334885e-05

Swap the order of a and b in code:

Run 1:

b time= 0.0001363120973110199

a time= 0.00011534709483385086

Run 2:

b time= 0.00021496228873729706

a time= 7.021520286798477e-05

Run 3:

b time= 0.00020176637917757034

a time= 6.876792758703232e-05

Conclusion:

The execution time for my_tensor.cuda().squeeze() and my_tensor.squeeze().cuda() are similar, and my_tensor.cuda().squeeze() might be a little bit faster.