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import torch
# https://pytorch.org/docs/stable/torch.html#random-sampling
class SamplingOpsModule(torch.nn.Module):
def __init__(self):
super(SamplingOpsModule, self).__init__()
def forward(self):
a = torch.empty(3, 3).uniform_(0.0, 1.0)
size = (1, 4)
weights = torch.tensor([0, 10, 3, 0], dtype=torch.float)
return len(
# torch.seed(),
# torch.manual_seed(0),
torch.bernoulli(a),
# torch.initial_seed(),
torch.multinomial(weights, 2),
torch.normal(2.0, 3.0, size),
torch.poisson(a),
torch.rand(2, 3),
torch.rand_like(a),
torch.randint(10, size),
torch.randint_like(a, 4),
torch.rand(4),
torch.randn_like(a),
torch.randperm(4),
a.bernoulli_(),
a.cauchy_(),
a.exponential_(),
a.geometric_(0.5),
a.log_normal_(),
a.normal_(),
a.random_(),
a.uniform_(),
)
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