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from typing import Union
import torch
class TestVersionedDivTensorExampleV7(torch.nn.Module):
def forward(self, a, b):
result_0 = a / b
result_1 = torch.div(a, b)
result_2 = a.div(b)
return result_0, result_1, result_2
class TestVersionedLinspaceV7(torch.nn.Module):
def forward(self, a: Union[int, float, complex], b: Union[int, float, complex]):
c = torch.linspace(a, b, steps=5)
d = torch.linspace(a, b)
return c, d
class TestVersionedLinspaceOutV7(torch.nn.Module):
def forward(
self,
a: Union[int, float, complex],
b: Union[int, float, complex],
out: torch.Tensor,
):
return torch.linspace(a, b, out=out)
class TestVersionedLogspaceV8(torch.nn.Module):
def forward(self, a: Union[int, float, complex], b: Union[int, float, complex]):
c = torch.logspace(a, b, steps=5)
d = torch.logspace(a, b)
return c, d
class TestVersionedLogspaceOutV8(torch.nn.Module):
def forward(
self,
a: Union[int, float, complex],
b: Union[int, float, complex],
out: torch.Tensor,
):
return torch.logspace(a, b, out=out)
class TestVersionedGeluV9(torch.nn.Module):
def forward(self, x):
return torch._C._nn.gelu(x)
class TestVersionedGeluOutV9(torch.nn.Module):
def forward(self, x):
out = torch.zeros_like(x)
return torch._C._nn.gelu(x, out=out)
class TestVersionedRandomV10(torch.nn.Module):
def forward(self, x):
out = torch.zeros_like(x)
return out.random_(0, 10)
class TestVersionedRandomFuncV10(torch.nn.Module):
def forward(self, x):
out = torch.zeros_like(x)
return out.random(0, 10)
class TestVersionedRandomOutV10(torch.nn.Module):
def forward(self, x):
x = torch.zeros_like(x)
out = torch.zeros_like(x)
x.random(0, 10, out=out)
return out
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