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 __repr__(self, *, tensor_contents: Any = None) -> str:  # type: ignore[override]
      return self._make_repr(
          format=self.format, canvas_size=self.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 4 for calling the BoundingBoxes obj's underlying member method code with example usage: obj.__repr__() and returns: self