class_docstr: This structure stores a list of boxes as a Nx4 torch.Tensor.
  It supports some common methods about boxes
  (`area`, `clip`, `nonempty`, etc),
  and also behaves like a Tensor
  (support indexing, `to(device)`, `.device`, and iteration over all boxes)
  
  Attributes:
      tensor (torch.Tensor): float matrix of Nx4. Each row is (x1, y1, x2, y2).
class_docstr_tok: ["This","structure","stores","a","list","of","boxes","as","a","Nx4","torch.Tensor",".","It","supports","some","common","methods","about","boxes","(","`","area","`",",","`","clip","`",",","`","nonempty","`",",","etc",")",",","and","also","behaves","like","a","Tensor","(","support","indexing",",","`","to","(","device",")","`",",","`",".device","`",",","and","iteration","over","all","boxes",")","Attributes",":","tensor","(","torch.Tensor",")",":","float","matrix","of","Nx4",".","Each","row","is","(","x1",",","y1",",","x2",",","y2",")","."]
docstr: Clone the Boxes.
  
  Returns:
      Boxes
docstr_tok: ["Clone","the","Boxes",".","Returns",":","Boxes"]
code: def clone(self) -> "Boxes":
      """
      Clone the Boxes.
  
      Returns:
          Boxes
      """
      return Boxes(self.tensor.clone())
  
imports: import math
  import numpy
  from enum import IntEnum, unique
  from typing import List, Tuple, Union
  import torch
  from torch import device
label_desc: Use image node_id 2 for calling the Boxes obj's underlying member method code with example usage: obj.clone() and returns: Boxes