class_docstr: Bbox sampling result.
  
  Example:
      >>> # xdoctest: +IGNORE_WANT
      >>> from mmdet.core.bbox.samplers.sampling_result import *  # NOQA
      >>> self = SamplingResult.random(rng=10)
      >>> print(f'self = {self}')
      self = <SamplingResult({
          'neg_bboxes': torch.Size([12, 4]),
          'neg_inds': tensor([ 0,  1,  2,  4,  5,  6,  7,  8,  9, 10, 11, 12]),
          'num_gts': 4,
          'pos_assigned_gt_inds': tensor([], dtype=torch.int64),
          'pos_bboxes': torch.Size([0, 4]),
          'pos_inds': tensor([], dtype=torch.int64),
          'pos_is_gt': tensor([], dtype=torch.uint8)
      })>
docstr: torch.Tensor: concatenated positive and negative boxes
docstr_tok: ["torch.Tensor",":","concatenated","positive","and","negative","boxes"]
code: def bboxes(self):
      """torch.Tensor: concatenated positive and negative boxes"""
      return torch.cat([self.pos_bboxes, self.neg_bboxes])
  
file_path: dinov2/dinov2/eval/segmentation_m2f/core/box/samplers/sampling_result.py
imports: import torch
label_desc: Use image node_id 2 for calling the SamplingResult obj's underlying member method code with example usage: obj.bboxes() and returns: torch