ptrblck:

would you like to get the exactly same values for each torch.randn call?


I believe that was the intention inferring from the context of the thread. Additionally, if we replace the above example with numpy we get the same behaviour.
import random
import numpy as np

random.seed(2019)
np.random.seed(2019)
rng = np.random.RandomState(2019)

d = rng.randn(4)
e = rng.randn(4)
c = rng.randn(4)
print(d, e, c)

every call to rand will produce a different result but the running the above code in a script over and over again will yield three tensors with different random values due to the call to randn but those values will be consistent across different runs of the same script.