With the recently introduced nested tensors, if I create a nested tensor:

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

and check its `type`

:

```
type(nt)
torch.Tensor
```

it just appears to be a regular `Tensor`

object. If in a code, I wanted to differentiate between a nested tensor and a regular tensor, how could I go about doing it as both `type(nt) == torch.Tensor`

and `isinstance(nt, torch.Tensor)`

will return `True`

?

One way I thought of is to use the fact that (currently) the `size`

method behaves differently, i.e., for a nested tensor it requires an argument otherwise it will raise a `RuntimeError`

. So, I could do:

```
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 a simpler way?