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# flake8: noqa
import torch
# seed
reveal_type(torch.seed()) # E: int
# manual_seed
reveal_type(torch.manual_seed(3)) # E: torch._C.Generator
# initial_seed
reveal_type(torch.initial_seed()) # E: int
# get_rng_state
reveal_type(torch.get_rng_state()) # E: {Tensor}
# bernoulli
reveal_type(torch.bernoulli(torch.empty(3, 3).uniform_(0, 1))) # E: {Tensor}
# multinomial
weights = torch.tensor([0, 10, 3, 0], dtype=torch.float)
reveal_type(torch.multinomial(weights, 2)) # E: {Tensor}
# normal
reveal_type(torch.normal(2, 3, size=(1, 4))) # E: {Tensor}
# poisson
reveal_type(torch.poisson(torch.rand(4, 4) * 5)) # E: {Tensor}
# rand
reveal_type(torch.rand(4)) # E: {Tensor}
reveal_type(torch.rand(2, 3)) # E: {Tensor}
# rand_like
a = torch.rand(4)
reveal_type(torch.rand_like(a)) # E: {Tensor}
# randint
reveal_type(torch.randint(3, 5, (3,))) # E: {Tensor}
reveal_type(torch.randint(10, (2, 2))) # E: {Tensor}
reveal_type(torch.randint(3, 10, (2, 2))) # E: {Tensor}
# randint_like
b = torch.randint(3, 50, (3, 4))
reveal_type(torch.randint_like(b, 3, 10)) # E: {Tensor}
# randn
reveal_type(torch.randn(4)) # E: {Tensor}
reveal_type(torch.randn(2, 3)) # E: {Tensor}
# randn_like
c = torch.randn(2, 3)
reveal_type(torch.randn_like(c)) # E: {Tensor}
# randperm
reveal_type(torch.randperm(4)) # E: {Tensor}
# soboleng
d = torch.quasirandom.SobolEngine(dimension=5)
reveal_type(d) # E: torch.quasirandom.SobolEngine
reveal_type(d.draw()) # E: {Tensor}
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