I’m not aware of a universal routine for that.
If you’re implying float tensors with known ranges and reasonable entropy, “surrogate” hash values based on standard reductions can be used, e.g.:

hashes=torch.quantize_per_tensor(torch.randn(1000,8), 0.1, 0, torch.qint32).int_repr().sum(-1) % 10
torch.bincount(hashes)

tensor(cpu,(10,)[ 92,  93, 106, 113,  94, 101,  97,  89, 100, 115])