code: def __new__(
      cls: Type[L],
      data: Any,
      *,
      categories: Optional[Sequence[str]] = None,
      dtype: Optional[torch.dtype] = None,
      device: Optional[Union[torch.device, str, int]] = None,
      requires_grad: Optional[bool] = None,
  ) -> L:
      tensor = cls._to_tensor(
          data, dtype=dtype, device=device, requires_grad=requires_grad
      )
      return cls._wrap(tensor, categories=categories)
  
file_path: vision/torchvision/prototype/tv_tensors/_label.py
imports: from __future__ import annotations
  from typing import Any, Optional, Sequence, Type, TypeVar, Union
  import torch
  from torch.utils._pytree import tree_map
  from torchvision.tv_tensors._tv_tensor import TVTensor
label_desc: Use image node_id 2 for calling the _LabelBase obj's underlying member method code with example usage: obj.__new__(cls, data) and returns: cls