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: Returns a dictionary of info about the object.
docstr_tok: ["Returns","a","dictionary","of","info","about","the","object","."]
code: def info(self):
      """Returns a dictionary of info about the object."""
      return {
          "pos_inds": self.pos_inds,
          "neg_inds": self.neg_inds,
          "pos_bboxes": self.pos_bboxes,
          "neg_bboxes": self.neg_bboxes,
          "pos_is_gt": self.pos_is_gt,
          "num_gts": self.num_gts,
          "pos_assigned_gt_inds": self.pos_assigned_gt_inds,
      }
  
file_path: dinov2/dinov2/eval/segmentation_m2f/core/box/samplers/sampling_result.py
imports: import torch
label_desc: Use image node_id 5 for calling the SamplingResult obj's underlying member method code with example usage: obj.info() and returns: dict