How can I differentiate between a PyTorch Tensor and a nested tensor?

Recently, PyTorch introduced the nested tensor. However, if I create a nested tensor, e.g.,

import torcha = torch.randn(20, 128)nt = torch.nested.nested_tensor([a, a], dtype=torch.float32)

and then look at its class type, it shows:

type(nt)torch.Tensor

i.e., the class type is just a regular PyTorch Tensor. So, type(nt) == torch.Tensor and isinstance(nt, torch.Tensor) will both return True.

So, my question is, is there a way to differentiate between a regular tensor and a nested tensor?

One way I can think of is that the size method to nested tensors (currently) works differently to that for regular tensors in that it requires an argument otherwise it raises a RuntimeError. So, a solution might be:

def is_nested_tensor(nt):    if not isinstance(nt, torch.Tensor):        return False    try:        # try calling size without an argument        nt.size()        return False    except RuntimeError:        return True    return False

but is there something simpler that doesn't rely on something like the size method not changing in the future?