class_docstr: This structure stores a list of rotated boxes as a Nx5 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)
docstr: Find boxes that are non-empty.
  A box is considered empty, if either of its side is no larger than threshold.
  
  Returns:
      Tensor: a binary vector which represents
      whether each box is empty (False) or non-empty (True).
docstr_tok: ["Find","boxes","that","are","non-empty",".","A","box","is","considered","empty",",","if","either","of","its","side","is","no","larger","than","threshold",".","Returns",":","Tensor",":","a","binary","vector","which","represents","whether","each","box","is","empty","(","False",")","or","non-empty","(","True",")","."]
code: def nonempty(self, threshold: float = 0.0) -> torch.Tensor:
      """
      Find boxes that are non-empty.
      A box is considered empty, if either of its side is no larger than threshold.
  
      Returns:
          Tensor: a binary vector which represents
          whether each box is empty (False) or non-empty (True).
      """
      box = self.tensor
      widths = box[:, 2]
      heights = box[:, 3]
      keep = (widths > threshold) & (heights > threshold)
      return keep
  
imports: import math
  from typing import List, Tuple
  import torch
  from detectron2.layers.rotated_boxes import pairwise_iou_rotated
  from .boxes import Boxes
label_desc: Use image node_id 7 for calling the RotatedBoxes obj's underlying member method code with example usage: obj.nonempty(threshold) and returns: keep