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from typing import Any
import torch
import torch_scatter
from packaging import version
reductions = ['sum', 'add', 'mean', 'min', 'max']
dtypes = [torch.half, torch.float, torch.double, torch.int, torch.long]
grad_dtypes = [torch.half, torch.float, torch.double]
if version.parse(torch_scatter.__version__) > version.parse("2.0.9"):
dtypes.append(torch.bfloat16)
grad_dtypes.append(torch.bfloat16)
devices = [torch.device('cpu')]
if torch.cuda.is_available():
devices += [torch.device('cuda:0')]
if torch.backends.mps.is_available():
devices += [torch.device('mps')]
def tensor(x: Any, dtype: torch.dtype, device: torch.device):
return None if x is None else torch.tensor(x, dtype=dtype, device=device)
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