Ok THANKs¡
One more thing. I have checked torchvision code at github and see that transform uses the random module from python. I understand that when using multiprocess the distribution of which samples are sampled from each thread are based on torch.random, and random is only user to (as example) decide if we rotate a particular image or not.
Moreover, how can one really ensure that we will not be replicating a sample? Because in (potentially) large datasets different seeds can end up producing the same number after several calls to random. As example consider:



numpy.random.seed(1)
numpy.random.randint(1,100,10)
array([10,  8, 64, 62, 23, 58,  2,  1, 61, 82])




numpy.random.seed(2)
numpy.random.randint(1,100,10)
array([41, 16, 73, 23, 44, 83, 76,  8, 35, 50]