Inheriting functional transforms for custom TVTensor

The latest pytorch v2 transforms allow creating custom TVTensors (see here). which allows registering functional transforms specific to a TVTensor type.

But what about applying the default F.transform on my custom TVTensor? I would need to convert TVTensor → torch.Tensor first, but I think the way I’m doing it creates a copy and torch.tensor() is deprecated.

from torchvision import tv_tensors
from torchvision.transforms.v2 import functional as F

class DepthmapVideoTVTensor(tv_tensors.TVTensor):
    Modified from the Video TVTensor here:
    def __new__(cls, data: Any, *, dtype: Optional[torch.dtype] = None, 
                device: Optional[Union[torch.device, str, int]] = None,
                requires_grad: Optional[bool] = None):
        tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad)
        if data.ndim != 4: #(seq_len, C, H, W)
            raise ValueError
        return tensor.as_subclass(cls)

    def __repr__(self, *, tensor_contents: Any = None) -> str: 
        return self._make_repr()

@F.register_kernel(functional=F.pad, tv_tensor_cls=DepthmapVideoTVTensor)
def pad_depthmap(depthmap, *args, **kwargs):
    print("Padding Depthmap!")
    depthmap_new = torch.tensor(depthmap)
    depthmap_new = F.pad(depthmap_new, *args, **kwargs)
    return tv_tensors.wrap(depthmap_new, like=depthmap)

Any better ideas? Thanks!