class_docstr: :class:`torch.Tensor` subclass for bounding boxes.
  
  .. note::
      There should be only one :class:`~torchvision.tv_tensors.BoundingBoxes`
      instance per sample e.g. ``{"img": img, "bbox": BoundingBoxes(...)}``,
      although one :class:`~torchvision.tv_tensors.BoundingBoxes` object can
      contain multiple bounding boxes.
  
  Args:
      data: Any data that can be turned into a tensor with :func:`torch.as_tensor`.
      format (BoundingBoxFormat, str): Format of the bounding box.
      canvas_size (two-tuple of ints): Height and width of the corresponding image or video.
      dtype (torch.dtype, optional): Desired data type of the bounding box. If omitted, will be inferred from
          ``data``.
      device (torch.device, optional): Desired device of the bounding box. If omitted and ``data`` is a
          :class:`torch.Tensor`, the device is taken from it. Otherwise, the bounding box is constructed on the CPU.
      requires_grad (bool, optional): Whether autograd should record operations on the bounding box. If omitted and
          ``data`` is a :class:`torch.Tensor`, the value is taken from it. Otherwise, defaults to ``False``.
code: def __new__(
      cls,
      data: Any,
      *,
      format: Union[BoundingBoxFormat, str],
      canvas_size: Tuple[int, int],
      dtype: Optional[torch.dtype] = None,
      device: Optional[Union[torch.device, str, int]] = None,
      requires_grad: Optional[bool] = None,
  ) -> BoundingBoxes:
      tensor = cls._to_tensor(
          data, dtype=dtype, device=device, requires_grad=requires_grad
      )
      return cls._wrap(tensor, format=format, canvas_size=canvas_size)
  
file_path: vision/torchvision/tv_tensors/_bounding_boxes.py
imports: from __future__ import annotations
  from enum import Enum
  from typing import Any, Mapping, Optional, Sequence, Tuple, Union
  import torch
  from torch.utils._pytree import tree_flatten
  from ._tv_tensor import TVTensor
label_desc: Use image node_id 2 for calling the BoundingBoxes obj's underlying member method code with example usage: obj.__new__(cls, data) and returns: cls