code: def wrap(wrappee, *, like, **kwargs):
      """Convert a :class:`torch.Tensor` (``wrappee``) into the same :class:`~torchvision.tv_tensors.TVTensor` subclass as ``like``.
  
      If ``like`` is a :class:`~torchvision.tv_tensors.BoundingBoxes`, the ``format`` and ``canvas_size`` of
      ``like`` are assigned to ``wrappee``, unless they are passed as ``kwargs``.
  
      Args:
          wrappee (Tensor): The tensor to convert.
          like (:class:`~torchvision.tv_tensors.TVTensor`): The reference.
              ``wrappee`` will be converted into the same subclass as ``like``.
          kwargs: Can contain "format" and "canvas_size" if ``like`` is a :class:`~torchvision.tv_tensor.BoundingBoxes`.
              Ignored otherwise.
      """
      if isinstance(like, BoundingBoxes):
          return BoundingBoxes._wrap(
              wrappee,
              format=kwargs.get("format", like.format),
              canvas_size=kwargs.get("canvas_size", like.canvas_size),
          )
      else:
          return wrappee.as_subclass(type(like))
  
file_path: vision/torchvision/tv_tensors/__init__.py
imports: import torch
  from ._bounding_boxes import BoundingBoxes, BoundingBoxFormat
  from ._image import Image
  from ._mask import Mask
  from ._torch_function_helpers import set_return_type
  from ._tv_tensor import TVTensor
  from ._video import Video
label_desc: Use image node_id 1 for calling a global function with example usage: wrap(wrappee) and returns: BoundingBoxes, wrappee